# Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion

## TL;DR

This paper introduces an unsupervised end-to-end method for discovering discrete linguistic units from speech, enabling effective voice conversion without labeled data, by combining a novel encoding scheme with adversarial training.

## Contribution

It proposes a new differentiable discrete encoding method (MBV) and an end-to-end unsupervised framework for speech unit discovery and voice conversion, outperforming previous approaches.

## Key findings

- Effective unsupervised speech unit discovery
- High-quality voice conversion with content preservation
- Strong performance in ZeroSpeech 2019 Challenge

## Abstract

We present an unsupervised end-to-end training scheme where we discover discrete subword units from speech without using any labels. The discrete subword units are learned under an ASR-TTS autoencoder reconstruction setting, where an ASR-Encoder is trained to discover a set of common linguistic units given a variety of speakers, and a TTS-Decoder trained to project the discovered units back to the designated speech. We propose a discrete encoding method, Multilabel-Binary Vectors (MBV), to make the ASR-TTS autoencoder differentiable. We found that the proposed encoding method offers automatic extraction of speech content from speaker style, and is sufficient to cover full linguistic content in a given language. Therefore, the TTS-Decoder can synthesize speech with the same content as the input of ASR-Encoder but with different speaker characteristics, which achieves voice conversion (VC). We further improve the quality of VC using adversarial training, where we train a TTS-Patcher that augments the output of TTS-Decoder. Objective and subjective evaluations show that the proposed approach offers strong VC results as it eliminates speaker identity while preserving content within speech. In the ZeroSpeech 2019 Challenge, we achieved outstanding performance in terms of low bitrate.