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KMID : 1022420210130040047
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Phonetics and Speech Sciences
2021 Volume.13 No. 4 p.47 ~ p.53
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End-to-end non-autoregressive fast text-to-speech
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Kim Wi-Back
Nam Ho-Sung
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Abstract
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Autoregressive Text-to-Speech (TTS) models suffer from inference instability and slow inference speed. Inference instability occurs when a poorly predicted sample at time step t affects all the subsequent predictions. Slow inference speed arises from a model structure that forces the predicted samples from time steps 1 to t-1 to predict the sample at time step t.
In this study, an end-to-end non-autoregressive fast text-to-speech model is suggested as a solution to these problems. The results of this study show that this model's Mean Opinion Score (MOS) is close to that of Tacotron 2 - WaveNet, while this model's inference speed and stability are higher than those of Tacotron 2 - WaveNet. Further, this study aims to offer insight into the improvement of non-autoregressive models.
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KEYWORD
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deep learning, neural network, speech synthesis, Text-to-Speech (TTS)
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FullTexts / Linksout information
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Listed journal information
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