Learning speaker embedding from text-to-speech

Jaejin Cho, Piotr Zelasko, Jesús Villalba, Shinji Watanabe, Najim Dehak

研究成果: Conference article査読

3 被引用数 (Scopus)

抄録

Zero-shot multi-speaker Text-to-Speech (TTS) generates target speaker voices given an input text and the corresponding speaker embedding. In this work, we investigate the effectiveness of the TTS reconstruction objective to improve representation learning for speaker verification. We jointly trained end-to-end Tacotron 2 TTS and speaker embedding networks in a self-supervised fashion. We hypothesize that the embeddings will contain minimal phonetic information since the TTS decoder will obtain that information from the textual input. TTS reconstruction can also be combined with speaker classification to enhance these embeddings further. Once trained, the speaker encoder computes representations for the speaker verification task, while the rest of the TTS blocks are discarded. We investigated training TTS from either manual or ASR-generated transcripts. The latter allows us to train embeddings on datasets without manual transcripts. We compared ASR transcripts and Kaldi phone alignments as TTS inputs, showing that the latter performed better due to their finer resolution. Unsupervised TTS embeddings improved EER by 2.06% absolute with regard to i-vectors for the LibriTTS dataset. TTS with speaker classification loss improved EER by 0.28% and 2.88% absolutely from a model using only speaker classification loss in LibriTTS and Voxceleb1 respectively.

本文言語English
ページ(範囲)3256-3260
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2020-October
DOI
出版ステータスPublished - 2020
外部発表はい
イベント21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
継続期間: 2020 10月 252020 10月 29

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

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