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Online Speaker Adaptation for WaveNet-based Neural Vocoders

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 Added by Qiuchen Huang
 Publication date 2020
and research's language is English




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In this paper, we propose an online speaker adaptation method for WaveNet-based neural vocoders in order to improve their performance on speaker-independent waveform generation. In this method, a speaker encoder is first constructed using a large speaker-verification dataset which can extract a speaker embedding vector from an utterance pronounced by an arbitrary speaker. At the training stage, a speaker-aware WaveNet vocoder is then built using a multi-speaker dataset which adopts both acoustic feature sequences and speaker embedding vectors as conditions.At the generation stage, we first feed the acoustic feature sequence from a test speaker into the speaker encoder to obtain the speaker embedding vector of the utterance. Then, both the speaker embedding vector and acoustic features pass the speaker-aware WaveNet vocoder to reconstruct speech waveforms. Experimental results demonstrate that our method can achieve a better objective and subjective performance on reconstructing waveforms of unseen speakers than the conventional speaker-independent WaveNet vocoder.



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We propose a linear prediction (LP)-based waveform generation method via WaveNet vocoding framework. A WaveNet-based neural vocoder has significantly improved the quality of parametric text-to-speech (TTS) systems. However, it is challenging to effectively train the neural vocoder when the target database contains massive amount of acoustical information such as prosody, style or expressiveness. As a solution, the approaches that only generate the vocal source component by a neural vocoder have been proposed. However, they tend to generate synthetic noise because the vocal source component is independently handled without considering the entire speech production process; where it is inevitable to come up with a mismatch between vocal source and vocal tract filter. To address this problem, we propose an LP-WaveNet vocoder, where the complicated interactions between vocal source and vocal tract components are jointly trained within a mixture density network-based WaveNet model. The experimental results verify that the proposed system outperforms the conventional WaveNet vocoders both objectively and subjectively. In particular, the proposed method achieves 4.47 MOS within the TTS framework.
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