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===== Tan, X., Chen, J., Liu, H., Cong, J., Zhang, C., Liu, Y., Wang, X., Leng, Y., Yi, Y., He, L., Soong, F., Qin, T., Zhao, S., & Liu, T.-Y. (2022). NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality. ''arXiv preprint arXiv:2205.04421''. ===== * 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.
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