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- W3049382401 abstract "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." @default.
- W3049382401 created "2020-08-21" @default.
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- W3049382401 date "2020-08-13" @default.
- W3049382401 modified "2023-09-23" @default.
- W3049382401 title "Online Speaker Adaptation for WaveNet-based Neural Vocoders" @default.
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- W3049382401 doi "https://doi.org/10.48550/arxiv.2008.06182" @default.
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