Editing State-of-the-art

== Theme: Template copy/paste but do not delete ==

=== Introduction ===
Briefly introduce your thematic focus and its significance in the field of speech technology.

=== Article summaries ===

* Article summaries and analyses: Each article receives a subsection including a summary (reference to RQ and hypothesis), critical analysis, and discuss its relevance to your theme.

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

=== Synthesis ===
Synthesis: Conclude with a section that synthesizes the key findings across the articles, highlighting any emerging trends, debates, or future research directions.

=== Contributors ===
Contributors: A list of contributors by contribution 

* Article Jones et al. 2023: YOUR NAME
* Article XXX: YOUR NAME
* Introduction: All
* Synthesis: All

== Low-resource ASR ==

=== Introduction ===
Our theme focuses on automatic speech recognition (ASR) of low-resource languages. Low-resource languages are often underrepresented in ASR due to the limited amount of data, limited amount of speakers, and low commercial impact. However, it is important for both preserving and encouraging the use of low-resource languages to allow for users to utilize ASR for their own language. Therefore, our theme is significant in the field of speech technology.

=== Article summaries ===

* Article summaries and analyses: Each article receives a subsection including a summary (reference to RQ and hypothesis), critical analysis, and discuss its relevance to your theme.

==== Wang, S., Rohdin, J., Plchot, O., Burget, L., Yu, K., & Cernocky, J. (2020). Investigation of Specaugment for Deep Speaker Embedding Learning. ''ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'', 7139–7143. <nowiki>https://doi.org/10.1109/ICASSP40776.2020.905348</nowiki> ====
* Summary: The article investigates the effectiveness of SpecAugment, a data augmentation method, for speaker verification tasks using TDNN and ResNet34 models with Softmax and AAMSoftmax loss functions. Experiments on NIST SRE 2016 Cantonese and Tagalog subsets and Voxceleb1 dataset show improved performance with SpecAugment, achieving 3.72% and 11.49% EER for NIST SRE 2016 Cantonese and Tagalog, respectively, and 1.47% EER for Voxceleb1. SpecAugment demonstrates promising results for speaker verification across different languages, enhancing system robustness without complex offline augmentation.
* RQ: How effective is SpecAugment, a data augmentation method originally proposed for speech recognition, when applied to speaker verification tasks across different languages, specifically Cantonese and Tagalog?
* Hypothesis: Applying SpecAugment, a data augmentation technique initially developed for speech recognition, to speaker verification tasks will lead to performance improvements across different languages, including Cantonese and Tagalog.
* Conclusion: Implementing SpecAugment for speaker verification tasks yields significant performance improvements across different languages. Specifically, the study demonstrates that SpecAugment, applied on-the-fly without complex offline augmentation methods, achieves state-of-the-art results in speaker verification tasks for Cantonese and Tagalog, as well as for the Voxceleb1 dataset.
* Critical observations: The critical observation of the article focuses on the implementation of SpecAugment for speaker verification tasks across various languages, particularly Cantonese and Tagalog, which are considered low-resource languages. The study demonstrates that SpecAugment, applied on-the-fly, effectively improves performance in speaker verification tasks for these languages, achieving significant reductions in Equal Error Rate (EER) compared to traditional methods. This highlights the potential of SpecAugment as a simple yet powerful augmentation technique, particularly beneficial for low-resource language processing tasks.
* Relevance: The relevance of the article to the topic of low-resource language Automatic Speech Recognition (ASR) lies in its exploration of SpecAugment as a data augmentation technique for speaker verification tasks in languages like Cantonese and Tagalog, which are considered low-resource. By demonstrating the effectiveness of SpecAugment in improving performance in speaker verification tasks for these languages, the study showcases a potential solution to the challenges posed by limited data availability in low-resource language ASR. This highlights SpecAugment as a valuable tool for enhancing ASR systems' robustness and accuracy in under-resourced linguistic contexts.

==== Zhang, Y., Han, W., Qin, J., Wang, Y., Bapna, A., Chen, Z., ... & Wu, Y. (2023). Google USM: Scaling automatic speech recognition beyond 100 languages. ''arXiv preprint arXiv:2303.01037''. ====

* Summary: Google's Universal Speech Model aims to develop an ASR model that will be able to perform speech recognition on all languages of the world. This paper leverages large amounts of unlabelled speech and text data from YouTube to train a multilingual-encoder that can then be used in fine-tuning on very small amounts of labelled data. This allows them to outperform Whisper<ref>Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023, July). Robust speech recognition via large-scale weak supervision. In ''International Conference on Machine Learning'' (pp. 28492-28518). PMLR.</ref> with significantly less labelled data, while also showing that this approach works positively for lower-resource languages.
* RQ: Can we leverage the large amounts of unlabelled speech data to perform massively multilingual ASR and speech translation?
* Hypothesis: By using a vast amount of unlabelled data, the encoder will learn speech representations that can be leveraged in fine-tuning and downstream tasks.
* Conclusion: Pre-training on unlabelled data is an effective way to improve multilingual performance while requiring much less labelled data.
* Critical observations: Although they keep mentioning that their performance is stellar on low-resource languages, no results were presented for these languages specifically. Most results are from multilingual datasets that might be imbalanced as well. Furthermore, the models and training data are not publicly available, making the research less approachable for improvements.
* Relevance: This paper is highly relevant for our theme as it aims to improve low-resource ASR through unlabelled data, which is an effective solution to the data scarcity problem.

==== Zhang, Y., Herygers, A., Patel, T., Yue, Z., & Scharenborg, O. (2023). ''Exploring data augmentation in bias mitigation against non-native-accented speech'' (arXiv:2312.15499). arXiv. <nowiki>http://arxiv.org/abs/2312.15499</nowiki> ====

* Summary: The study aimed to investigate the impact of data augmentation techniques on the performance of Flemish Automatic Speech Recognition (ASR) systems for both native Flemish speakers and those with non-native accents. Specifically, the research focused on addressing biases against non-native-accented Flemish speech. Various data augmentation methods were applied to augment the training data, and the performance of the ASR system was evaluated using both native and non-native speakers' speech samples. The results suggested that tailored data augmentation techniques can lead to improved ASR system performance for both native and non-native-accented Flemish speech. This finding highlights the potential of data augmentation in mitigating bias and enhancing the accuracy of ASR systems across diverse speaker demographics.
* RQ: What is the optimal type of data augmentation, in terms of reducing bias against non-native-accented Flemish in a Flemish ASR system, when applied to both native Flemish and non-native-accented Flemish?
* Hypothesis: Applying specific types of data augmentation techniques, tailored to address bias against non-native-accented Flemish speech, will lead to improved performance in a Flemish Automatic Speech Recognition (ASR) system for both native Flemish and non-native-accented Flemish speakers.
* Conclusion: The study concluded that employing tailored data augmentation techniques can significantly improve the performance of Flemish Automatic Speech Recognition (ASR) systems, particularly in mitigating biases against non-native-accented speech. By augmenting the training data with techniques specifically designed to address the characteristics of non-native accents, the ASR system demonstrated notable enhancements in accuracy for both native and non-native speakers. These findings underscore the importance of considering diversity in training data and utilizing appropriate augmentation strategies to enhance the robustness and inclusivity of ASR systems.
* Critical observations: The performance of Flemish Automatic Speech Recognition (ASR) systems can be significantly improved through the use of tailored data augmentation techniques. Specifically, augmenting the training data with methods designed to address the characteristics of non-native accents resulted in notable enhancements in accuracy for both native and non-native speakers. This observation highlights the importance of considering diversity in training data and employing appropriate augmentation strategies to enhance the inclusivity and robustness of ASR systems.
* Relevance:  Low-resource languages often suffer from limited available data for training ASR systems, which can lead to poor performance, especially for speakers with non-native accents. This study demonstrates that tailored data augmentation techniques can substantially improve the accuracy of ASR systems, even in scenarios with limited training data.By addressing the challenges faced by speakers with non-native accents, the paper contributes valuable insights into how ASR technology can be adapted and optimized for low-resource languages. It underscores the importance of developing strategies that account for linguistic diversity and accent variations, ultimately making ASR systems more inclusive and effective in diverse linguistic contexts. Therefore, the findings of this study are highly relevant for researchers and practitioners working on ASR for low-resource languages, offering practical approaches to enhance system performance and usability in such settings.

=== Synthesis ===
Synthesis: Conclude with a section that synthesizes the key findings across the articles, highlighting any emerging trends, debates, or future research directions.

=== Contributors ===
Contributors: A list of contributors by contribution 

* Artice Wang et al., 2019: Xinyi Ma
* Article Google USM: Scaling automatic speech recognition beyond 100 languages: Ömer Tarik
* Article Zhang et al., 2023: Xinyi Ma
* Introduction: Ömer Tarik
* Synthesis: All
== Language-specific Text-To-Speech ==

=== Introduction ===
State-of-the-art Text-to-Speech systems have different performances based on the language they are developed for and trained on. We choose to focus on language-specific TTS and provide a review on state-of-the-art techniques to synthesise languages that are not English. This does not necessarily restrict to Low-Resourced Languages, but it will include them.

=== Article summaries ===

* Article summaries and analyses: Each article receives a subsection including a summary (reference to RQ and hypothesis), critical analysis, and discuss its relevance to your theme.

==== Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2021). A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages: Interspeech 2021. Proc. Interspeech 2021, 16–20. <nowiki>https://doi.org/10.21437/Interspeech.2021-1565</nowiki> ====

* Summary: The article provides an overview of strategies for text-to-speech (TTS) for low-resource langauges (LRLs), focusing on Multilingual Data strategies. More specifically, this article presents an evaluation of the results of the previous studies on LRLs TTS, an evaluation of the influence of data augmentation techniques employed on the performance of the models and the proposal of a new measure to evaluate the performance of multilingual vs. monolingual systems with different evaluation metrics, namely MultiLingual Model Effect (''MLME''). The performance of strategies analysed is also checked by verifying how different factors influence it.
* RQ: 
* Hypothesis:
* Conclusion: Multilingual approaches are more effective in training for LRLs. The factors that affect the performance are: 
** target language data ratio between corresponding multilingual and monolingual models;
** target language data balance ratio over total training data
** amount of target language data.
* Critical observations: The paper only focuses on multilingual data strategies, and justifies the choice by saying that multispeaker data are harder to collect for LRLs. Even though I understand the reaoning behind this, I believe this is not entirely true. On one hand, it is true indeed that it is harder to find many speakers for a LRLs, since oftentimes such languages are also minority languages. On the other hand, collecting multispeaker data mean that each speaker can contribute with a very small amount of data and still get enough of them. This means that by adopting multispeaker TTS techniques, we don't need to record one speaker for a long time, but rather multiple speakers for a short time. 
* Relevance: The most relevant outcome of this study, especially for LRLs TTS, is that the '''''language family is not relevant for the selection of the target-source language pair''', no matter the architecture.'' In my opinion, the conclusions of this paper are also relevant for medium-resourced languages and in general for the synthesis of non-standard speech and for all the types of speech that are not widely covered by the research so far. 

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

=== Synthesis ===
Synthesis: Conclude with a section that synthesizes the key findings across the articles, highlighting any emerging trends, debates, or future research directions.

=== Contributors ===
* Article Do, et al. (2021) 'A Systematic Review and Analysis of Multilingual Data Strategies in Text-to-Speech for Low-Resource Languages': Alice Vanni
* Article Jones et al. 2023: YOUR NAME
* Introduction: All
* Synthesis: All

== Theme: Non-Language-specific Text-To-Speech ==

=== Introduction ===
TTS systems have significantly advanced over time, achieving remarkable intelligibility and near-human naturalness in synthetic voices through deep learning advancements. However, the naturalness of synthetic voice remains limited to sentences, and lacks the expressivity found in human conversation such as appropriate emotion, prosody and style. Despite these limitations, natural TTS, particularly expressive speech synthesis, plays a crucial role in achieving human-like speech and enhancing the engagement of synthesized speech. Moreover, it facilitates the broader adoption of TTS technology across various domains within the field of speech technology. In this context, our group focuses on the theme of TTS naturalness with two interconnected subthemes: exploring advanced models and relevant theories. By addressing these subthemes, we aim to provide a comprehensive overview of the current state-of-the-art in TTS naturalness.

=== Article summaries ===

==== Huang, R., Huang, J., Yang, D., Ren, Y., Liu, L., Li, M., ... & Zhao, Z. (2023, July). Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models. In ''International Conference on Machine Learning'' (pp. 13916-13932). PMLR. ====

* Summary: The article introduces "Make-An-Audio," a system utilizing a prompt-enhanced diffusion model for TTS generation, aiming to improve the naturalness and expressiveness of synthesized audio.
* RQ: How does the model improve the naturalness of TTS?
* Hypothesis: By introducing pseudo prompt enhancement and spectrogram autoencoders, the model can effectively utilize unsupervised language-free data and higher-level semantic understanding to enhance the naturalness and expressiveness of speech synthesis.
* Conclusion: "Make-An-Audio" successfully enhances the naturalness and expressiveness of speech synthesis, achieving state-of-the-art performance in evaluations.
* Critical observations: The performance of "Make-An-Audio" is still partly dependent on extensive data and complex model training. In addition, there is still space for improvement in expressing the emotions and rhythms of human conversations.
* Relevance: The "Make-An-Audio" system presented in the paper offers an effective solution to the limitations in naturalness and expressiveness currently faced by TTS.

==== APA Citation of NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality ====

* Summary: NaturalSpeech proposes a system for converting text to speech (TTS) that achieves human-level quality. It leverages a variational autoencoder (VAE) to bridge the gap between text and speech waveforms.
* RQ (Research Question): Can a TTS system achieve speech quality indistinguishable from humans?
* Hypothesis: By incorporating a VAE and specific techniques to improve the model's understanding of text and speech features, NaturalSpeech can generate speech indistinguishable from humans.
* Conclusion: The paper argues that NaturalSpeech achieves human-level speech quality based on statistical measures (MOS and CMOS) in human evaluations.
* Critical Observations: The evaluation relies on subjective human ratings, which might be influenced by factors beyond speech quality.The research focuses on a single benchmark dataset, limiting generalizability.The paper doesn't explore how NaturalSpeech performs on diverse speaking styles or accents.

* Relevance: This is related to my study because it provides a definition of human-level quality, and this particular model has achieved the highest Mean Opinion Score (MOS) recorded thus far. Hence, I am considering using this model as a basis for my study.

==== Noufi, C., May, L., & Berger, J. (2023). Context, Perception, Production: A Model of Vocal Persona. ''PsyArXiv. July'', ''28''. ====

* Summary: This article introduces a contextualized production-perception model of vocal persona, developed through qualitative analysis of interviews with voice and performance experts. It emphasizes the influence of context on an individual's vocal expression, reflecting the intricacies of human communication.
* RQ: What is the relationship between context, vocal expression, and identity?
* Hypothesis: It is a qualititative study and does not hve a formulated hypothesis.  Instead of attempting to falsify a hypothesis as in most quantitative studies, it explores answers to the research question through thematic analysis.
* Conclusion: Speakers actively select different vocal personas and adjust relevant vocal expressions in response to the surrounding context, facilitating a transition in the perception of persona.
* Critical observations: The proposal of the vocal persona model and general conclusions are based on interviews with 21 voice and performance experts, which may have limitations in terms of subjective bias and generalizability beyond this specific context.
* Relevance: This study underscores the necessity for speakers to adapt their speaking styles to accommodate different social contexts, highlighting the significance of context in vocal expression. It proposes the incorporation of vocal persona into expressive vocal synthesis with a three-spoke model and a framework for persona-guided vocalization, enriching the framework of TTS naturalness and expressiveness.

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

==== APA Citation of an article ====

* Summary:
* RQ:
* Hypothesis:
* Conclusion:
* Critical observations:
* Relevance:

=== Synthesis ===
Synthesis: Conclude with a section that synthesizes the key findings across the articles, highlighting any emerging trends, debates, or future research directions.

=== Contributors ===
Contributors: A list of contributors by contribution 

* Article Huang et al. 2023: Yilan Wei
* Article 'Context, Perception, Production: A Model of Vocal Persona': Chenyi Lin
* Article NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality: Yi Lei
* Introduction: Chenyi Lin
* Synthesis: All
<references />

== Theme: ASR ==

=== Introduction ===
The rapid evolution of Automatic Speech Recognition (ASR) technology has been a cornerstone in advancing how humans interact with machines, propelling us towards more seamless and intuitive communication avenues. The focus on ASR technology underscores its pivotal role across a myriad of applications, from enhancing accessibility and providing robust customer support solutions to creating immersive interactive entertainment experiences. Among the most intriguing challenges in this domain is the recognition and interpretation of complex human sentiments such as sarcasm and humor. These nuanced forms of expression, deeply embedded in human language, present unique challenges for ASR systems due to their reliance on contextual cues, background knowledge, and the subtle modulations in tone that conventional speech recognition systems often miss. Our exploration is driven by the imperative to bridge this gap, aiming to refine ASR technology's ability to discern and process these complex sentiments. 

=== Article summaries ===

* Article summaries and analyses: Each article receives a subsection including a summary (reference to RQ and hypothesis), critical analysis, and discuss its relevance to your theme.

==== Wang S, Yang C H H, Wu J, et al. Can whisper perform speech-based in-context learning[J]. arXiv preprint arXiv:2309.07081, 2023. ====

* Summary: The study investigates Whisper ASR models' in-context learning capabilities and proposes a novel SICL method for test-time adaptation without gradient descent, achieving significant WER reductions.
* RQ: The research explores whether Whisper models can perform speech-based in-context learning and how to leverage in-context examples for test-time adaptation efficiently.
* Hypothesis: The hypothesis is that Whisper models can adapt at test time using SICL with context examples from specific dialects or speakers.
* Conclusion: SICL significantly improves ASR performance for Chinese dialects without gradient descent, with k-NN enhancing SICL's efficiency.
* Critical observations: Correct LID settings and k-NN example selection improve Whisper's inference, with language-level adaptation outperforming speaker-level adaptation.
* Relevance: The study is relevant for understanding and enhancing the application of large pre-trained models in automatic speech recognition and dialect adaptation.

==== Sungjoo Ahn and Hanseok Ko. “Background Noise Reduction via Dual-Channel Scheme for Speech Recognition in Vehicular Environment.” IEEE Transactions on Consumer Electronics 51, no. 1 (February 2005): 22–27. <nowiki>https://doi.org/10.1109/TCE.2005.1405694</nowiki>. ====

* Summary: The paper proposes a dual-channel noise reduction method aimed at enhancing speech recognition systems within vehicular environments, characterized by significant noise challenges. The authors argue that existing single-channel methods fall short in effectively improving speech recognition performance due to inherent noise complexities in vehicles. The proposed method leverages a high-pass filter combined with an eigen-decomposition front-end processing technique, tested against real multi-channel vehicular corpus. Experimental results indicated a notable improvement in speech recognition performance using various microphone arrangements, showcasing the superiority of the dual-channel approach over traditional single-channel methods.
* RQ: How can the performance of speech recognition systems in vehicular environments be improved through a dual-channel noise reduction scheme?
* Hypothesis: The paper hypothesizes that employing a dual-channel noise reduction scheme, which integrates a high-pass filter with eigen-decomposition front-end processing, can significantly enhance speech recognition performance in noisy vehicular environments by effectively distinguishing speech from background noise.
* Conclusion: Authors concluded that their dual-channel noise reduction method, especially when augmented with a high-pass filter and enhanced eigen-decomposition processing, substantially improves speech recognition accuracy in vehicular settings. The method outperformed standard single-channel noise reduction approaches and showed considerable promise in overcoming the challenges posed by vehicular background noise, thereby validating the hypothesis.
* Critical observations: The study successfully demonstrates the effectiveness of a dual-channel approach in a challenging noise environment. However, the practical deployment of such systems, including the economic implications and the adaptability across different vehicle models and noise conditions, remains less explored. Additionally, while the study marks a significant improvement over existing methods, the scalability of this approach in terms of computational demand and real-time processing capabilities could benefit from further investigation.
* Relevance: This thesis is relevant to the topics in enhancing speech recognition technology area. The innovative approach of combining a dual-channel noise reduction scheme with a high-pass filter and eigen-decomposition method provides a substantial leap forward in developing more reliable and efficient speech recognition systems.

==== Zhang, Wangyou, and Yanmin Qian. “Weakly-Supervised Speech Pre-Training: A Case Study on Target Speech Recognition.” arXiv, June 29, 2023. <nowiki>http://arxiv.org/abs/2305.16286</nowiki>. ====
* Summary: This study introduces a new way to teach computers to understand speech by focusing on one person's voice in a noisy place, like when many people talk at once. This method, called TS-HuBERT, uses extra information about the speaker's voice to improve speech recognition, especially in challenging situations with lots of background noise. Tests showed that TS-HuBERT does a better job than other similar methods, making it a promising approach for better understanding speech in noisy environments.
* RQ: Can we use extra information about who is speaking to help computers better recognize speech in noisy settings?
* Hypothesis: By using additional information about the speaker, the TS-HuBERT method can focus on the target speaker's voice more effectively, even when other voices or noises are present.
* Conclusion: TS-HuBERT improves speech recognition by focusing on the target speaker's voice, outperforming other current methods. This approach is particularly useful for recognizing speech in noisy places where many people are talking at once.
* Critical observations: 
** TS-HuBERT can be adjusted to different speech recognition tasks, showing its versatility.
** Although it needs extra information about the speaker's voice, this method greatly enhances the computer's ability to focus on and understand the target speaker in noisy situations.
** There is still room for improvement, especially in very noisy environments, indicating potential areas for future research.
* Relevance: This study is directly relevant to the topic to help computers understand speech better in challenging environments, like when many people are talking at the same time. By focusing on a specific speaker's voice, TS-HuBERT could make speech recognition technology more effective in real-world situations.

==== Bae, S., Kim, J.-W., Cho, W.-Y., Baek, H., Son, S., Lee, B., Ha, C., Tae, K., Kim, S., & Yun, S.-Y. (2023). Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification. Retrieved from <nowiki>https://arxiv.org/abs/2305.14032v4</nowiki> ====
Summary: The study introduces a novel approach for respiratory sound classification, leveraging a pretrained Audio Spectrogram Transformer (AST) model, alongside a new Patch-Mix augmentation technique and Patch-Mix Contrastive Learning. These methods are designed to address the challenges of medical data scarcity and enhance model performance on the ICBHI dataset. The approach sets a new state-of-the-art performance benchmark, improving the classification Score by 4.08% over previous methods.
*RQ: Can a pretrained Audio Spectrogram Transformer (AST) model, combined with Patch-Mix augmentation and Patch-Mix Contrastive Learning, effectively improve respiratory sound classification, especially in the context of the ICBHI dataset?
* Hypothesis: The hypothesis posits that leveraging a pretrained AST model, which has been trained on large-scale visual and audio datasets, can be effectively generalized to respiratory sound classification tasks. Additionally, it suggests that the introduction of Patch-Mix augmentation and Patch-Mix Contrastive Learning can further enhance model performance by addressing the scarcity of medical data and the challenges of leveraging such data for deep learning models.
* Conclusion: The study concludes that the proposed approach, combining a pretrained AST model with Patch-Mix augmentation and Patch-Mix Contrastive Learning, significantly enhances respiratory sound classification. This method achieved state-of-the-art performance on the ICBHI dataset, demonstrating the effectiveness of the proposed techniques in improving classification accuracy in the face of limited medical data availability and complex data characteristics.
* Critical observations:
** Pre-training on both visual and audio domains using the AST model shows substantial improvements in generalizing to respiratory sound classification tasks.
** The Patch-Mix augmentation technique, which randomly mixes patches between different samples, and the Patch-Mix Contrastive Learning method, which distinguishes mixed representations in the latent space, effectively mitigate the overfitting issue and enhance model robustness.
** The study's methodology offers a significant performance increase, demonstrating the potential of attention-based models and contrastive learning in medical sound classification.
* Relevance: This research holds relevance to Automatic Speech Recognition (ASR) by showcasing the utility of attention-based models like the AST in capturing long-range dependencies in audio data. The techniques developed for respiratory sound classification, particularly the effective use of pretrained models and innovative augmentation strategies, can inform similar challenges in ASR, including dealing with limited training data and enhancing model generalization across diverse audio inputs.

==== Gairola1, S., Tom, F., Kwatra1, N., & Jain1, M. (2021). RESPIRENET: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting. Retrieved from <nowiki>https://arxiv.org/abs/2011.00196v2</nowiki> ====
*Summary: The study introduces RespireNet, a CNN-based model for classifying respiratory sounds, particularly focusing on addressing the challenge posed by the small size of the largest available respiratory dataset, ICBHI, which consists of only 6,898 breathing cycles. The study proposes a suite of novel techniques including device-specific fine-tuning, concatenation-based augmentation, blank region clipping, and smart padding to efficiently utilize this small dataset. Extensive evaluation on the ICBHI dataset demonstrates significant improvements over state-of-the-art results for 4-class classification by 2.2%.
* RQ: Can a simple CNN-based model, when combined with specific data utilization techniques, accurately classify respiratory sounds from a limited-sized dataset, overcoming the challenges of data scarcity and variability?
* Hypothesis: The study hypothesizes that even with a small dataset, a simple network architecture, if supplemented with innovative techniques for data utilization and augmentation, can accurately classify respiratory sounds. These techniques include addressing dataset characteristics such as device variability, class imbalance, and varying audio lengths that traditionally inhibit effective DNN training.
* Conclusion: RespireNet, along with the proposed data utilization techniques, significantly improves the accuracy of respiratory sound classification, achieving new state-of-the-art performance on the ICBHI dataset for both 2-class and 4-class classification tasks. The study concludes that focusing on efficient data utilization and addressing specific dataset characteristics can compensate for the limitations posed by small-sized datasets.
* Critical observations:
*# Transfer learning from pre-trained ImageNet models proves beneficial, suggesting that even unrelated domain knowledge can improve model performance.
*# Concatenation-based augmentation effectively addresses class imbalance, significantly improving classification of underrepresented classes.
*# Device-specific fine-tuning is essential for generalizing across different recording devices, highlighting the impact of hardware variability on model performance.
*# Techniques like smart padding and blank region clipping are crucial for dealing with variable-length audio samples and irrelevant frequency regions, respectively, ensuring the model focuses on relevant features.
* Relevance: The challenges and solutions presented in this study have direct implications for ASR, especially in scenarios where data is scarce or highly variable. Techniques such as smart data augmentation, device-specific adjustments, and focusing on relevant audio features can be applied to improve ASR systems' robustness and accuracy in diverse conditions. Furthermore, the emphasis on efficient data utilization and simple model architectures can inspire similar approaches in ASR research to overcome data-related limitations.

==== Yang, R., Lv, K., Huang, Y., Sun, M., Li, J., & Yang, J. (2023). Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable. ''Applied Sciences'', 13(6956). <nowiki>https://doi.org/10.3390/app13126956</nowiki> ====
*Summary: The paper introduces a deep neural network named Blnet for classifying respiratory sounds, incorporating features from ResNet, GoogleNet, and self-attention mechanisms to tackle the non-IID (not independently and identically distributed) data problem and imbalanced data issues. The model demonstrated improved performance on the ICBHI 2017 respiratory sound database, showcasing a significant advancement in sensitivity and specificity rates over existing methods.
* RQ: How can a deep neural network be optimized for classifying respiratory sounds to facilitate the early detection of respiratory diseases, considering challenges such as non-IID data and imbalanced datasets?
* Hypothesis: The integration of ResNet, GoogleNet, and self-attention mechanisms into a deep neural network, alongside a two-stage training process and mix-up data augmentation within clusters, can significantly improve the classification accuracy of respiratory sounds, even with imbalanced and non-IID data challenges.
* Conclusion: The Blnet model successfully addressed the challenges of non-IID and imbalanced datasets in respiratory sound classification, achieving a 4.22% improvement in average score and a 12.61% improvement in sensitivity over state-of-the-art results. This performance enhancement underscores the efficacy of the proposed network architecture and training strategies.
* Critical observations:
** The two-stage training process and the introduction of a blocking variable proved effective in managing non-IID data, suggesting the importance of considering data distribution in deep learning models.
** Mix-up data augmentation within clusters and the use of multiple input transformations (STFT and WT) were critical in addressing data imbalance and enhancing model robustness.
** The self-attention mechanism played a key role in capturing global dependencies within the data, improving the model's feature extraction capabilities.
** Simplifying the loss function to handle a four-class classification task as two independent binary classification tasks was found to enhance training effectiveness.
* Relevance: The techniques and findings of this study have direct implications for ASR systems, particularly in enhancing model performance with non-IID and imbalanced datasets. The methods for improving feature extraction and classification in the context of respiratory sound analysis can inform approaches to noise reduction, signal processing, and robust model training in ASR technologies. Furthermore, the attention mechanisms and data augmentation strategies could be adapted to improve ASR systems' ability to deal with diverse and challenging acoustic environments.

==== Zhou, Rui, Xian Li, Ying Fang, and Xiaofei Li. “Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR.” arXiv, February 21, 2024. <nowiki>http://arxiv.org/abs/2402.13511</nowiki>. ====

*Summary: This paper introduces Mel-FullSubNet, a network designed for enhancing speech quality and automatic speech recognition (ASR) performance. It focuses on improving both the clarity of speech and its recognizability by machines in noisy conditions. The technique enhances Mel-spectrograms of speech, which can then be used directly for ASR or converted back into speech waveforms using a neural vocoder. The method combines full-band and sub-band network processing, proving to be more effective for ASR and speech quality enhancement compared to previous approaches.
* RQ: Can Mel-spectrogram enhancement via Mel-FullSubNet improve both speech quality and automatic speech recognition performance in noisy conditions?
* Hypothesis: By enhancing Mel-spectrograms using the Mel-FullSubNet, which combines full-band and sub-band processing, both speech quality and ASR performance can be significantly improved in noisy environments.
* Conclusion: Mel-FullSubNet successfully enhances speech quality and ASR performance, outperforming several existing methods. It shows particular strength in providing cleaner speech signals and more accurate ASR results by focusing on Mel-spectrogram enhancement and efficiently leveraging neural vocoders for waveform generation.
* Critical observations: 
** Mel-FullSubNet demonstrates superior generalization to unseen data and environments, a critical advantage for real-world applications.
** The method's efficacy is underscored by its performance on various datasets, indicating its robustness and adaptability.
** While Mel-FullSubNet requires more computational resources due to its neural vocoder component, its efficiency and output quality justify the additional cost.
* Relevance:This study is directly relevant to the topic to the challenge of enhancing speech recognition systems in noisy conditions, a common problem in real-world applications. By focusing on Mel-spectrogram enhancement, Mel-FullSubNet provides a novel approach that benefits both speech clarity and ASR accuracy, making it a valuable reference for further research in speech processing technology.

'''Castro, S., Hazarika, D., Pérez-Rosas, V., Zimmermann, R., Mihalcea, R., & Poria, S. (2019). Towards Multimodal Sarcasm Detection (An Obviously Perfect Paper). arXiv:1906.01815v1.'''

* '''Summary:''' The paper introduces a novel approach to sarcasm detection by leveraging multimodal data. Recognizing that sarcasm often involves incongruities not just in text but also in vocal tone and facial expressions, the authors propose the first dataset, MUStARD, for sarcasm detection using audio-visual cues alongside textual data. This dataset, compiled from popular TV shows, is annotated for sarcasm, aiming to facilitate the development of models that can better understand sarcasm through the integration of multiple modes of communication.
* '''RQ:''' How can incorporating multimodal cues (textual, audio, and visual) improve the automatic classification of sarcasm compared to relying on textual data alone?
* '''Hypothesis:''' The paper hypothesizes that the inclusion of multimodal information (audio and visual cues, along with textual data) can significantly enhance the performance of sarcasm detection models, reducing the relative error rate by up to 12.9% in F-score when compared to models that use only individual modalities.
* '''Conclusion''': The research demonstrates that multimodal models significantly outperform unimodal variants in sarcasm detection, with a notable reduction in error rate. The findings underscore the importance of considering multiple communication cues, beyond just text, for effectively identifying sarcasm. The MUStARD dataset is also introduced as a valuable resource for future research in multimodal sarcasm detection.
* '''Critical Observations:'''

# Sarcasm detection benefits from multimodal analysis, including textual, audio, and visual data, highlighting the complex nature of sarcasm as a communicative act that often relies on the interplay of various signals.
# The MUStARD dataset fills a critical gap in research resources, providing a foundation for exploring how different modalities contribute to the understanding of sarcasm.
# The study's methodology, focusing on a balanced dataset and robust multimodal feature extraction techniques, sets a precedent for future work in this area.

* '''Relevance:''' This research is highly relevant to my thesis topic. It pushes the boundaries of sarcasm detection by moving beyond text analysis to include audio and visual cues, offering insights into more holistic approaches to understanding human communication. The findings and the MUStARD dataset can significantly impact the development of more nuanced and effective computational models for detecting sarcasm and other complex emotional or figurative language use cases.

'''Zhang, Yazhou, Yang Yu, Qing Guo, Benyou Wang, Dongming Zhao, Sagar Uprety, Dawei Song, Qiuchi Li, and Jing Qin. “CMMA: Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations,” n.d.'''
* '''Summary:''' This study introduces the CMMA dataset for benchmarking multi-affection detection in Chinese multi-modal conversations, focusing on sentiment, emotion, sarcasm, and humor. The dataset comprises annotations from a variety of TV series to reflect diverse affective expressions and supports both single-task and multi-task learning paradigms for affective computing research.
* '''RQ:''' How multi-modal cues and conversational context influence the detection of multiple affects, including sentiment, emotion, sarcasm, and humor, in Chinese multi-party conversations?
* '''Hypothesis:''' Benchmarking Multi-Affection Detection in Chinese Multi-Modal Conversations" likely centers on the premise that incorporating multi-modal data (text, video, audio) and conversational context significantly improves the accuracy and effectiveness of detecting multiple affects (sentiment, emotion, sarcasm, humor) in multi-party conversations. The study posits that the interplay between different modalities and the contextual understanding of conversations enhances the model's ability to interpret complex human affective expressions.
* '''Conclusion:''' The findings demonstrate that conversational context and multi-modal data significantly enhance affect detection tasks. The study also highlights the importance of multi-affect annotation for understanding complex human communications, suggesting the CMMA dataset as a valuable resource for future affective computing research.
* '''Critical observations:''' While the dataset offers comprehensive insights into multi-affect detection, its focus on Chinese TV series may limit its applicability across different linguistic and cultural contexts. Additionally, the inherent subjectivity of affect annotation poses challenges to achieving unbiased affect detection.
* '''Relevance:''' This study is pertinent to my thesis as it provides an opportunity to delve into how various feature fusion methods impact the accuracy of sarcasm recognition in Mandarin using multimodal data. Additionally, the CMMA dataset is highly beneficial to my research because it is among the few Chinese datasets that include labels for sarcasm, offering a valuable resource for studying sarcasm recognition within Mandarin-specific contexts using multimodal information.

=== Synthesis ===
The articles reviewed collectively contribute to the ASR field, showing a trend towards multimodal data use, context awareness, and noise reduction techniques to address complexities in human speech such as sarcasm and humor. Key observations include the importance of integrating audio, visual, and textual data for better sarcasm detection, the effectiveness of dual-channel noise reduction in vehicular environments, and the application of deep learning for respiratory sound classification and speech enhancement in noisy settings. Challenges mentioned across these studies involve data scarcity, handling diverse dialects, and computational demands. Future research directions suggest a focus on improving ASR systems' adaptability across languages and cultures, better managing non-IID and imbalanced data, and enhancing emotional intelligence in speech recognition. These findings indicate ongoing efforts to make ASR technologies more intuitive and effective in complex human-machine interactions.

=== Contributors ===
 
Contributors: A list of contributors by contribution 

* Article Wang et al. (2023): Yaling Deng
* Article Sungjoo Ahn and Hanseok Ko (2005): Dongwen Zhu
* Article Zhang and Qian (2023): Dongwen Zhu
* Article Zhou et al. (2024): Dongwen Zhu
* Article Bae et al. (2023): Soogyeong Shin
* Article Gairola et al. (2021): Soogyeong Shin
* Article Yang et al. (2023): Soogyeong Shin
* Article Castro et al. (2019) : Erin Shi
* Article Zhang et al. (2021): Youyang Cai
* Introduction: All
* Synthesis: All
<ref>Can Whisper perform speech-based in-context learning?</ref>

== Miscellaneous ==
This last section corresponds to articles that did not fit well inside other themes.

=== Introduction ===
Voice technology, transcending the traditional boundaries of speech recognition and synthesis, has emerged as a transformative force in a multitude of sectors, revolutionizing not just how we communicate with machines, but also how sound is manipulated and perceived in our digital world. This segment, aptly titled "None of the Above," delves into the innovative applications of voice technology beyond the realms of text-to-speech (TTS) and automatic speech recognition (ASR). It encompasses a wide array of technologies including voice enhancement, noise reduction, accent modification, and speaker seperation, each playing a pivotal role in refining and enriching the auditory experience. These advancements underscore the versatility and depth of voice technology, pushing the boundaries of what is possible in audio quality, clarity, and customization. 

=== Article summaries ===
* Article summaries and analyses: Each article receives a subsection including a summary (reference to RQ and hypothesis), critical analysis, and discuss its relevance to your theme.

==== Grimm, M., Kroschel, K., & Narayanan, S. (2007, April). Support vector regression for automatic recognition of spontaneous emotions in speech. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07 (Vol. 4, pp. IV-1085). IEEE. ====

* Summary: The paper presents methods for estimating emotions expressed spontaneously in speech, using Support Vector Regression (SVR). It evaluates three emotion primitives—valence, activation, and dominance—showing SVR's superiority over Fuzzy Logic and Fuzzy k-Nearest Neighbor classifiers in accuracy and correlation with human assessments.
* RQ: How to estimate emotions under the conditions of (1) nonacted, spontaneous speech and (2) non-categorical, quasicontinuous emotional content.
* Hypothesis: SVR can more accurately estimate emotions in speech compared to traditional classifiers, given its ability to handle continuous emotion primitives and complex non-linear relationships in data.
* Conclusion: SVR outperforms Fuzzy Logic and k-Nearest Neighbor classifiers in estimating emotions from speech, achieving lower classification errors and higher correlations with reference emotions. This underscores SVR's suitability for continuous-valued emotion estimation in spontaneous speech.
* Critical observations: SVR yields the lowest mean classification errors and highest correlation coefficients for emotion estimation. In addition, Feature selection indicates that using 20 features suffices for accurate emotion estimation across different classifiers.
* Relevance: This study advances automatic emotion recognition in speech, crucial for improving human-machine interaction and developing emotionally intelligent systems. Future work will investigate designing a real-time system using the algorithms. The advantage of continuous-valued estimates of the emotional state of a person could be used to build an adaptive emotion tracking system that is capable to adapt to individual personalities and long-term moods.

=== Synthetically improving foreign-accented speech recognition ===

==== Introduction ====
More often than not, speech corpora either contain only native speech, or the non-native subset is significantly underrepresented. At the same time, gender and foreign accent are the most salient factors contributing to changes in the acoustics of speech. However, not only are there numerous possible combinations of L1 and L2s, but the annotation and labelling os recordings to a suitable degree (e.g. age of L2 acquisition, country of origin, L1, L2 proficiency, language of education etc. are all factors that should be reported in order to make the speech resources reliable and usable) are laborious and expensive.

In light of these challenges, methods of synthetical data augmentation have been recently explored in the literature. While creating synthetically-accented data through accent conversion models (ACMs) is a straightforward, inexpensive, and oof-the-shelf approach, it is not without limitations and the degree to which recognition performance is improved through such approaches depends on several factors. The following three articles provide some insight into these approaches and highlight both major advantages and persistent challenges.

==== Zhao et al. (2018): Accent conversion using phonetic posteriograms ====
'''Summary''': Accent conversion (AC) means transforming non-native speech to sound as if the speaker had a native accent, or vice-versa. The main challenge faced in traditional methods of voice conversion is decoupling the speaker’s voice quality from their pronunciation (i.e. teasing apart accent information and keeping everything else acoustically unchanged). Additionally, when mapping source spectra from a native speaker into the acoustic space of an L2 speaker, previous attempts focus on acoustic similarity: changing formants- and pitch trajectories, blending spectral envelopes. The alternative used here is, in turn, is phonetic similarity, which maps source to target based on an intermediate phonetic label.
The phonetic posteriograms are computed using a DNN-based acoustic model. The distance between these phonetic posterior feature vectors is calculated to find the closest pairs of frames between source (native) and target (L2) speakers. The frame pairs are used to train a GMM. The two baselines used are acoustic similarity matching and dynamic time warping.

Experimental setup: get Kaldi DNN acoustic model, train it on Librispeech data, get native English speech (CMUArctic) and non-native recordings (Hindi, Korean, Arabic), use STRAIGHT for speech decomposition, MFCC extraction, train GMMs (128 components), synthesize speech by reconstructing spectrograms and adding aperiodicity.

'''RQ:''' How can accent-related features be successfully decoupled from speaker-related features, to achieve non-native to native voice conversion while preserving speech quality?

'''Results:''' Synthesized results were compared to baselines through listening tasks using Mechanical Turk (rating acoustic quality, speaker identity y/n, nativeness of resynthesized speech):

* significantly higher acoustic quality ratings compared to baselines.
* comparable speaker identity scores.
* strong preferrence for posteriogram conversions by native EN speakers as more ‘native-like‘ compared to baselines and original L2 utterances.

'''Critical observations:''' This paper addressed the opposite issue, namely converting foreign-accented speech to sound like native one (mainly for educational purposes). This still means you need to figure out which features are related to accent, and which features are related to anything else, but is arguably the easier thing to do, as it requires to drop information instead of successfully adding it. Additionally, the approach is not entirely explainable, because posteriograms are encoder features and it's not always transparent what is learned to be most relevant. Lastly, this approach likely works increasingly worse the fewer speakers there are in a dataset. Even if you accented speech data, one speaker can only have one accent, so in case the number of speakers is small, the model might learn to encode speaker identity instead of accent features.

'''Relevance:''' It is important to know that given enough speakers and enough data, accent features can be decoupled from other speech features and dropeed to obtain a higher perceived 'nativeness' of the speech. 

==== Jin et al. (2023): Voice-preserving zero-shot multiple accent conversion ====
'''Summary:''' Separating accent from speaker identity is usually the hardest, because each speaker in the dataset has one single accent. Previous attempts at doing this include:

* use adversarial learning to get a discriminator to wipe out speaker-dependent information from content embeddings.
* quantization of different features in speech to obscure undesired information.

The main problem with conventional approaches to conversion is that they very often require available utterances with the same text in both source and target accent, making their applicability very limited. Alternatively, different approaches require either training or fine-tuning on the input utterances.

The current paper uses a pronunciation encoder, an acoustic encoder, and a HiFiGAN voice decoder. During training, the model minimises reconstruction loss between input and output mel-spectrograms. The pronunciation encoder synthesizes accent-dependent pronunciation sequences using accent IDs. The acoustic encoder mapss MFCCs and periodicity features to a single vector, while adversarial training removes accent information. Lastly, the decoder reconstructs waveforms from the processed features. The model is evaluated on audio quality, speaker similarity, and accent conversion effectiveness.

'''Results:''' Results indicate it maintains comparable audio quality to the original, maintains speaker similarity, and is efficient in replicating perceived nativeneess. However, listeners struggled to identify synthesized accents if they were unfamiliar with the target language (e.g. a native US listener could not classify a Korean accent on English as such, but a bilingual Korean-American listener could). Overall, the paper presents one of the best performing ACMs, that is able to preserve both speaker identity and acoustic quality during conversion. 

'''Critical observations:''' I think this paper achives a lot given that it's zero shot, but I am a bit critical about just how 'zero-shot' it truly is. They use a pre-trained acoustic model and while they do not require accent labels or speaker IDs, it seems that their training set contains over 24h of accented speech for all accents that they're synthesizing in. Additionally, none of their code is openly available, which is understandable for a private corporation like Meta, but it's still a bit disappointing.

==== Klumpp et al. (2023): Synthetic cross-accent data augmentation for ASR ====
'''Summary:''' Foreign-accentes speech is usually underrepresented in, if not absent from speech corpora. Auxiliary input (learned accent embeddings, intermediate wav2vec2.0 representations) can address the decreased ASR recognition on this type of speech; the challenge remains that of achieving good accent conversion while preserving source speaker voice characteristics. The current approach builds on a pre-existing ACM by Jin et al. (2023) -- see above -- and aims to provide synthetic ASR training data using it. Phonetic knowledge is crucially injected into training to improve accent-specific pronunciation, and learnable accent representations are introduced to allow for variable accent strengths and adaptability to unseen accents.

The experimental setup involved evaluating two ASR models using Librispeech data. The first model (Base) utilized an efficient memory transformer followed by a recurrent neural transducer (RNNT), while the second model (HuBERT) had a similar structure with adjustments in channel configurations and dropout probabilities. The ASR models were tested on Librispeech data and accents from L2-Arctic corpus and Accented Vox Populi (AVP) dataset. 

In experiments, the baseline ASR systems were trained without synthetic accented speech data, then evaluated. Three additional ASR models were trained with a combination of real and synthetic accented data, using a ratio of 80% real and 20% synthetic data. The ratio remained consistent across all accents. Finally, learned accent embeddings from L2-Arctic samples were visualized using t-SNE plots to assess their suitability for encoding accent information in an Accent Conversion Model (ACM).

'''RQ:''' Is it possible to improve ASR of accented speech with synthetic samples of a particular accent?

'''Results:''' The inclusion of one synthetic accent during ASR training had a positive effect on recognition results for that particular accent, which was a clear indicator that the ACM was able to synthesize a sufficient degree of accentedness. At the same time, HuBERT'd performance decreased with the use of synthetic data, likely due to the fact that it was not pre-trained on any and fine-tuning did not do enough. The Base model, which was trained from scratch, had a much grater benefit from the synthetic data. Notably, even when all seven accents were introduced in training, this did not improve performance on other unseen accents.

Overall, including one synthetic accent improved performance on that accent; and including several accents improved performance on those accents, but none of the conditions improved recognition on accents not seen in training. Additionally, pre-trained HuBERT did not benefit much from additional synthetic data fine-tuning, whereas a model trained from scratch saw much greater benefit from this approach.

'''Critical observations:''' Again, none of this replicable because the code is not available. It would have been also interesting to see a bit more ASR models be tested on this; this particular comparison does highlight the pre-trained/trained from scratch distinction in performance on this task, but there are other models that are seemingly good candidates and were not included.

'''Relevance:''' The authors show the potential for using synthetically accented data as a data augmentation approach to improve ASR performance on foreign-accented speech.

==== General insights ====
The synthesis of accented speech as a data augmentation method in ASR is promising for improving recognition performance on non-native speech. The three articles reviewed provide valuable insights into accent conversion methods and their implications for ASR systems. Zhao et al. (2018) shows the effectiveness of phonetic posteriograms in converting foreign-accented speech to sound more native-like and successfully decouples accent-related features from other speech characteristics. Jin et al. (2023) proposed a zero-shot multiple accent conversion approach, maintaining audio quality and speaker identity during conversion, albeit with limitations in accent classification for unfamiliar listeners. Klumpp et al. (2023) extended this work by integrating synthetic accented speech data into ASR training, showing improvements in recognition performance on the trained accents. However, the effectiveness varied depending on the model architecture, with pre-trained models benefiting less from synthetic data than models trained from scratch. Despite promising results, the lack of code availability and limited generalizability to unseen accents pose challenges for broader adoption. Overall, while accent conversion models offer a promising strategy for data augmentation in ASR, further research should focus on generalization and replicability for real-world applications.

==== References ====
Jin, M., Serai, P., Wu, J., Tjandra, A., Manohar, V., & He, Q. (2023, June). Voice-preserving zero-shot multiple accent conversion. In ''ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'' (pp. 1-5). IEEE.

Klumpp, P., Chitkara, P., Sarı, L., Serai, P., Wu, J., Veliche, I. E., ... & He, Q. (2023). Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition. ''arXiv preprint arXiv:2303.00802''.

Zhao, G., Sonsaat, S., Levis, J., Chukharev-Hudilainen, E., & Gutierrez-Osuna, R. (2018, April). Accent conversion using phonetic posteriorgrams. In ''2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)'' (pp. 5314-5318). IEEE.

=== Accent Modification ===

==== Introduction ====
Accents play a crucial role in shaping the unique characteristics of speech, reflecting an individual's linguistic background and cultural identity. However, the presence of foreign accents can sometimes pose challenges, particularly in the speaking test for language proficiency assessment.

==== Finkelstein, L., Zen, H., Casagrande, N., Chan, C., Jia, Y., Kenter, T., Petelin, A., Shen, J., Wan, V., Zhang, Y., Wu, Y., & Clark, R. (2022). Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks. Google LLC. Retrieved from <nowiki>https://arxiv.org/abs/2208.13183</nowiki> ====
'''Summary''': This paper presents a practical approach for accent transfer tasks in text-to-speech (TTS) synthesis, where aspects of one speaker's speech are transferred to another speaker's speech. The authors address the challenge of creating high-quality transfer models that are also stable and suitable for user-facing applications. They propose a two-step training process involving a Tacotron-based accent transfer model and a robust CHiVE-BERT TTS system. The CHiVE-BERT system is trained on synthetic data generated by the Tacotron model, which results in high-quality audio with transferred accents while preserving speaker characteristics.

'''RQ:''' How can text-to-speech systems be trained to achieve accent transfer effectively and stably, without compromising the quality or usability of the synthesized speech?

'''Hypothesis:''' By training a robust TTS system on synthetic data generated by a less stable but high-quality accent transfer model, it is possible to achieve a balance between quality and stability in accent transfer tasks.

'''Conclusion:''' The study concludes that the proposed two-step training approach, using synthetic data generated by a Tacotron-based model to train a CHiVE-BERT system, yields reliable performance in terms of naturalness and accent transfer capability. The quality loss associated with the switch to synthetic data is within acceptable bounds, and the final system produces high-quality audio that maintains the original speakers' characteristics.

'''Critical observations:''' The authors note that the quality of the final system is affected by the intermediate Tacotron model, with some accents showing significant quality loss, particularly for female speakers in British English. Training on synthetic data can result in lower quality loss compared to using human recordings, possibly due to the reduced variance in synthetic data. The choice of vocoder, synthesizer, and the balance between synthetic and human recordings are critical in the training process, with the final system benefiting from a combination of both.

'''Relevance:''' The research on accent transfer in TTS systems aligns closely with my focus on accent modification for Turkish immigrants in Dutch oral exams. The methodologies explored for synthesizing and transferring accents can be adapted to develop tools that neutralize accents, enhancing exam fairness by ensuring evaluations are based on language skills rather than accent. 

==== Li, W., Tang, B., Yin, X., Zhao, Y., Li, W., Wang, K., Huang, H., Wang, Y., & Ma, Z. (2020). Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech. arXiv preprint arXiv:2005.09271. Retrieved from <nowiki>https://arxiv.org/abs/2005.09271</nowiki> ====
'''Summary:''' This paper presents an end-to-end accent conversion framework aimed at transforming non-native accents into native accents while preserving the speaker's voice timbre. The proposed system introduces reference encoders to utilize multi-source information and optimizes the model architecture using GMM-based attention for improved synthesized performance. Experimental results show significant improvements in acoustic quality and native accent while retaining the non-native speaker's voice identity.

'''RQ:''' How can accent conversion be improved to better transform non-native accents into native accents in a way that maintains the original speaker's voice identity? 

'''Hypothesis:''' Incorporating reference encoders and optimizing the model architecture with GMM-based attention will enhance the quality and naturalness of converted speech, leading to more effective accent conversion.

'''Conclusion:''' Incorporating reference encoders and optimizing the model architecture with GMM-based attention will enhance the quality and naturalness of converted speech, leading to more effective accent conversion.

'''Critical observations:''' The paper highlights the importance of prosodic and expressive information in accent conversion, which is effectively captured by the reference encoder. The GMM-based attention mechanism is found to be more stable and powerful for feature representation compared to traditional windowed attention.

'''Relevance:''' The research is relevant to accent modification efforts, particularly in language learning and pronunciation training contexts. The proposed accent conversion techniques could be applied to develop tools that help non-native speakers improve their pronunciation and reduce their accents, thereby enhancing communication and integration in societies where the target language is spoken natively. 

==== Zang, X., Weng, F., & Zang, X. (2022). Foreign Accent Conversion using Concentrated Attention. In 2022 IEEE International Conference on Knowledge Graph (ICKG). Retrieved from <nowiki>https://ieeexplore.ieee.org/document/978-1-6654-5101-7</nowiki> ====
'''Summary:''' This paper introduces a novel method for foreign accent conversion (FAC) utilizing Phonetic Posteriorgrams (PPGs) and Log-scale Fundamental frequency (Log-FO) to address phonetic and prosody mismatches. The proposed approach employs concentrated attention to enhance the alignment of input sequences and mel-spectrograms, selecting the top k highest score values in the attention matrix row by row. The method is evaluated through objective metrics and demonstrates improved voice naturalness, speaker similarity, and accent similarity.

'''RQ:''' How can foreign accent conversion be improved to achieve better alignment and naturalness in synthesized speech while preserving the source speaker's identity? 

'''Hypothesis:''' Implementing concentrated attention in the foreign accent conversion process will result in more accurate alignment of input sequences with mel-spectrograms, leading to improved accent conversion quality and naturalness in synthesized speech.

'''Conclusion:''' The proposed method using concentrated attention for foreign accent conversion delivers comparable or better results than previous methods in terms of voice naturalness and accent similarity. The concentrated attention mechanism effectively focuses on the most relevant frames for better alignment and synthesized speech quality.

'''Critical observations:''' The concentrated attention mechanism is found to be beneficial for achieving better alignment between input sequences and target sequences, resulting in improved speech synthesis.

'''Relevance:''' The research is relevant to the field of speech synthesis and voice conversion, particularly for applications that require the alteration of accents while maintaining the original speaker's voice characteristics. This work contributes to the development of systems that can aid in language learning, dubbing, and other scenarios where accent modification is beneficial, enhancing the quality and naturalness of synthesized speech. 

==== Contributors: ====
The section ''Synthetically improving foreign-accented speech recognition'' was written by Maria Tepei.
<references />The section Accent Modification was written by Chenyu Li:

==== Contributors ====
Contributors: A list of contributors by contribution 

* Article Finkelstein et al.(2022): Chenyu Li
* Article Li et al.(2020): Chenyu Li
* Article Zang et al(2022): Chenyu Li
* Article Grimm et al.(2007): Yining Lei
* Introduction: Chenyu Li
* Synthesis: All