Editing State-of-the-art (section)

== 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.

=== Speech Emotion Recognition ===

==== 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.


'''Z. Huang, M. Dong, Q. Mao, and Y. Zhan, “Speech emotion recognition using cnn,” in Proceedings of the 22nd ACM international conference on Multimedia,pp. 801–804,ACM, 2014.'''

'''Summary'''：The paper introduces a CNN model that processes input data in two stages, using unlabeled samples for candidate feature extraction and then learning discriminative features under semi-supervision.

'''RQ'''：The main research question is how to efficiently and automatically extract discriminative sentiment features from speech signals for sentiment recognition, especially in complex scenarios where the speaker and environment change.

'''Hypothesis'''：The main research question is how to efficiently and automatically extract discriminative sentiment features from speech signals for sentiment recognition, especially in complex scenarios where the speaker and environment change.

'''Conclusion'''：The semi-CNN model can effectively learn emotionally skewed features to achieve consistent and robust performance in speech emotion recognition tasks.

'''Critical observations'''：Semi-CNN models benefit from a two-stage feature learning process that initially extracts candidate features without labeling the data. The use of novel objective functions to improve feature saliency, orthogonality, and discrimination helps to enhance the robustness of the model.

'''Relevance'''：It is important to facilitate human-computer interaction by improving the accuracy and reliability of speech emotion recognition systems. It contributes to the development of the field of affective computing and may influence the development of more sensitive and adaptive SER systems.

=== 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. 

=== Speech Separation ===

==== Zegers, J. (2019). CNN-LSTM models for multi-speaker source separation using Bayesian hyperparameter optimization. arXiv preprint arXiv:1912.09254. ====
'''Summary:''' This paper explores the use of Bayesian hyperparameter optimization for parallel CNN-LSTM models in the task of multi-speaker source separation (MSSS). Experiments were conducted with mixtures from the WSJ0 corpus and found that parallel CNN-LSTM models performed better than individual CNN or LSTM models. 

'''Research Question (RQ):''' How does Bayesian hyperparameter optimization affect the performance of parallel CNN-LSTM models in multi-speaker source separation?

'''Hypothesis:''' The hypothesis was that the Bayesian optimization technique would find a better hyperparameter set that allows the parallel CNN-LSTM model to outperform individual CNNs or LSTMs in MSSS.

'''Conclusion:''' The study concluded that models with more trainable parameters in the LSTM portion performed better and that parallel CNN-LSTM models with Bayesian hyperparameter optimization outperformed the other models tested.

'''Critical Observations:''' The LSTM part of the model was crucial for performance, and bidirectional LSTMs performed better than unidirectional ones. Also, the study noted that more trainable parameters in the LSTM were generally preferred.

'''Relevance:''' This research is relevant for advancements in speech processing, specifically in improving source separation techniques which is a foundational task in many audio processing applications.

==== Isik, Y., Roux, J. L., Chen, Z., Watanabe, S., & Hershey, J. R. (2016). Single-channel multi-speaker separation using deep clustering. arXiv preprint arXiv:1607.02173 ====
'''Summary:''' This study improved the baseline system for speaker-independent multi-speaker separation using deep clustering with an end-to-end signal approximation objective. By optimizing the model with enhancements like regularization, larger temporal context, and a deeper architecture, significant improvements in signal-to-distortion ratio and word error rate were achieved.

'''Research Question (RQ):''' Can the performance of speaker-independent multi-speaker separation be improved by using deep clustering with an end-to-end training approach?

'''Hypothesis:''' The authors hypothesized that incorporating an end-to-end signal approximation objective would lead to better performance in speech separation.

'''Conclusion:''' The paper concluded that the deep clustering approach with an end-to-end signal approximation objective greatly improved signal quality metrics and reduced speech recognition error rates, contributing to solving the cocktail party problem.

'''Critical Observations:''' The model performed well even with different numbers of speakers, and the addition of a signal approximation objective substantially reduced the word error rate when integrated with automatic speech recognition systems.

'''Relevance:''' This research contributes to solving complex audio environments' speech recognition challenges, aiding the development of better voice-activated systems that can function effectively in real-world conditions.

==== Maiti, S., Ueda, Y., Watanabe, S., Zhang, C., Yu, M., Zhang, S., & Xu, Y. (2023). EEND-SS: Joint end-to-end neural speaker diarization and speech separation for flexible number of speakers. In 2022 IEEE Spoken Language Technology Workshop (SLT) (pp. 480-487). IEEE. ====
'''Summary:''' The paper presents EEND-SS, a framework that integrates speaker diarization, speech separation, and speaker counting into a single end-to-end trainable model. It demonstrated improved performance over single-task models and enhanced speaker counting for a flexible number of speakers.

'''Research Question (RQ):''' Can an integrated framework that combines speaker diarization and speech separation improve performance over models that address these tasks separately?

'''Hypothesis:''' The authors posited that a joint model incorporating speaker diarization, speech separation, and speaker counting would perform better than individual models tackling each task separately.

'''Conclusion:''' The study concluded that the EEND-SS framework could outperform single-task baselines in both diarization and separation metrics and improved speaker counting performance.

'''Critical Observations:''' A key observation was that jointly learning to separate and diarize helped the model perform better in diarization, particularly in less overlapped conditions, suggesting better generalization.

'''Relevance:''' The results of this study are highly relevant for multi-speaker environments, improving the performance and applicability of voice recognition systems in scenarios with a variable number of speakers. Each of these studies contributes to the field of speech processing, advancing our understanding and capability in separating and recognizing speech in challenging audio scenarios.

=== Speech Synthesis Evaluation ===
'''Le Maguer, S., King, S., & Harte, N. (2024). The limits of the Mean Opinion Score for speech synthesis evaluation. ''Computer Speech & Language'', ''84'', 101577. <nowiki>https://doi.org/10.1016/j.csl.2023.101577</nowiki>'''

'''Summary:''' The paper critically evaluates the Mean Opinion Score (MOS) as an evaluation metric of synthetic speech. The authors conduct 4 experiments related to the Blizzard Challenge to assess the stability and reliability of MOS, the influence of varying quality systems on MOS, and how the introduction of modern technologies affects the scoring of historical systems.

'''Research Question (RQ):''' How reliable and stable is the Mean Opinion Score (MOS) when used for speech synthesis evaluation, especially with modern speech synthesis technologies that closely approximate human speech?

'''Hypothesis:'''  MOS, despite being a standard evaluation metric, is a relative score influenced by the presence of both lower and higher quality systems in the evaluation set and may not adequately reflect the advancements in modern speech synthesis technologies.

'''Conclusion:''' The study concludes that MOS is influenced by the relative quality of the systems being evaluated and suggests that MOS has reached its limits in terms of effectiveness for evaluating modern speech synthesis technologies. New evaluation protocols that better capture the nuances of current systems are needed.

'''Critical Observations:''' The authors observe that MOS tends to be relative rather than absolute, its scores can vary over time, and it is sensitive to the presence of anchors. The presence of high-quality modern systems can influence the MOS of historical systems, often leading to a compression of scores.

'''Relevance:''' This research is relevant for the field of speech synthesis evaluation, particularly as the technology has reached a quality close to human speech. It challenges the current predominant reliance on MOS and argues for the development of more sophisticated evaluation protocols that can better analyze modern synthesis technologies.

'''O’Mahony, J., Oplustil-Gallegos, P., Lai, C., & King, S. (2021). Factors Affecting the Evaluation of Synthetic Speech in Context. 11th ISCA Speech Synthesis Workshop (SSW 11), 148–153. <nowiki>https://doi.org/10.21437/SSW.2021-26</nowiki>'''

'''Summary:''' The paper examines factors that influence the evaluation of synthetic speech in context, particularly as Text-to-Speech (TTS) synthesis approaches naturalness limits for isolated sentences. It explores the effect of instructions given to participants, the impact of between-sentence textual context dependency, and the sensitivity of Mean Opinion Score (MOS) to prosodic differences in synthetic speech.

'''Research Question (RQ):''' How do various factors such as listener instructions, between-sentence textual context dependency, and prosodic realizations of synthetic speech affect the evaluation of synthetic speech in context?

'''Hypothesis:'''  The authors hypothesize that the wording of instructions given to listeners, the textual context of sentences, and the prosody of synthetic speech can significantly affect the MOS ratings, potentially causing variations in the assessment of speech synthesis quality.

'''Conclusion:''' The study finds that listener instructions significantly impact MOS ratings, with 'appropriateness' and 'naturalness' being interpreted differently. Textual context dependency does not significantly affect ratings, and listeners are sensitive to prosodic differences. The MOS is an appropriate paradigm for evaluating prosodic differences in synthetic speech.

'''Critical Observations:''' The authors observe that despite non-context-aware synthesis, utterances presented in context receive higher MOS ratings than those in isolation. Furthermore, participants' interpretation of 'appropriateness' contributes to higher ratings in context, and MOS ratings are sensitive to substantial prosodic differences.

'''Relevance:''' This research is relevant for advancing TTS evaluation methods. It suggests that the MOS rating system needs to consider the influence of contextual factors and prosody for long-form speech synthesis evaluation, indicating a shift from traditional sentence-level assessment paradigms.

=== 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 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
* Article Z.Huang et al.(2014):Siqi Zheng
* Introduction: Chenyu Li
* Synthesis: All
==== Subsections: ====
The section ''Synthetically improving foreign-accented speech recognition'' was written by Maria Tepei.
<references />The section Accent Modification was written by Chenyu Li

The section Speech Separation was written by Sherry Yu-Ting Yeh

The section Speech Synthesis Evaluation was written by Brandi Hongell