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.

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 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.

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 Google USM: Scaling automatic speech recognition beyond 100 languages: Ömer Tarik
• Introduction: Ömer Tarik
• Synthesis: All

Language-specific Text-To-Speech

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

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

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 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.

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
• Article NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality: Yi Lei
• Introduction: All
• Synthesis: All