Wed-3-5-10 Learning Speaker Embedding from Text-to-Speech

Abstract: 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 Text-to-Speech (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 further enhance these embeddings. 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, and shows that the latter performed better due to their finer resolution. Unsupervised TTS embeddings improved EER by 2.06% absolute w.r.t. i-vectors for the LibriTTS dataset. TTS with speaker classification loss improved EER by 0.28% and 2.88% absolutely from a state-of-the-art method using only speaker loss in LibriTTS and Voxceleb1 respectively.