Tencent America LLC

Jiachen Lian

Dong Yu

Chunlei Zhang

A method for disentangled variational speech representation learning for voice conversion, performed by at least one processor, is provided. The method includes receiving input speech segments, encoding the input speech segments via a shared encoder to generate a speaker embedding and a content embedding, encoding the posterior distributions of the speaker embedding via a speaker encoder and encoding the posterior distributions of the content embedding via a content encoder to obtain encoded results, and decoding the encoded results by concatenating the encoded results to obtain a reconstructed speech output.

Techniques for disentangled variational speech representation learning for zero-shot voice conversion

An unsupervised text to speech system utilizing a lexicon to map input text to the phoneme sequence, which is expanded to the frame-level forced alignment with a speaker-dependent duration model. An alignment mapping module that converts the forced alignment to the unsupervised alignment (UA). Afterword, a Conditional Disentangled Sequential Variational Auto-encoder (C-DSVAE), serving as the self-supervised TTS AM, takes the predicted UA and a target speaker embedding to generate the mel spectrogram, which is ultimately converted to waveform with a neural vocoder.

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Palo Alto, CA, United States of America

Jiachen Lian