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===== Peiró-Lilja, A., & Farrús, M. (2020). Naturalness Enhancement with Linguistic Information in End-to-End TTS Using Unsupervised Parallel Encoding. ''Interspeech 2020'', 3994–3998. <nowiki>https://doi.org/10.21437/Interspeech.2020-1788</nowiki> ===== * Summary: The paper explores enhancing the naturalness of synthesized speech in E2E-TTS systems by incorporating linguistic features like POS tags and punctuation into the Tacotron 2 model, aiming to improve prosody to resemble human speech more closely. * RQ: How can linguistic information be integrated into the Tacotron 2 system to improve the naturalness of synthesized speech prosody? * Hypothesis: The hypothesis is that by embedding POS tags and punctuation locations as additional linguistic features into the Tacotron 2 system, the synthesized speech will exhibit improved naturalness and prosody, making it more similar to human speech. * Conclusion: The study concludes that the incorporation of linguistic features through a parallel encoder significantly improves the naturalness of synthesized speech. The authors proposed two different architectures for the parallel encoder: one based on convolutional and recurrent layers (2DConv+BiLSTM) and another composed of bidirectional recurrent and linear layers (BiGRU+Linear). Both architectures aimed to process the binary matrix representing POS tags and punctuation locations. The results from objective tests and perceptual evaluations indicated that the model with the 2DConv+BiLSTM parallel encoder performed the best in terms of naturalness, as it more closely matched human pitch contours and overall speech quality. * Critical observations: Critically, the paper notes that while both parallel encoder architectures showed improvements over the Tacotron 2 baseline, the 2DConv+BiLSTM version provided better results in terms of naturalness. However, it also introduced a slight increase in Mel Cepstral Distortion (MCD), suggesting a trade-off between naturalness and certain acoustic quality metrics. The BiGRU+Linear model, despite being lighter and faster, underperformed in perceptual tests, possibly due to its reduced complexity and higher cepstral distortion. * Relevance: The findings of this research are relevant for the development of more natural and human-like E2E-TTS systems, which have applications in various domains such as automatic dialogue systems, storytelling, and voice assistants. By enhancing the prosody of synthesized speech, these systems can provide more engaging and realistic interactions, improving user experience and accessibility. Furthermore, the study contributes to the broader field of speech synthesis by demonstrating the potential of unsupervised parallel encoding of linguistic features to improve speech naturalness.
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