Unsupervised Acoustic Unit Representation Learning for Voice Conversion using WaveNet Auto-encoders
This work addresses the challenge of generating high-quality speech from discrete latent representations in voice conversion, which is important for applications like speech synthesis and assistive technologies, though it is incremental as it builds on existing VQVAE methods.
The paper tackled the problem of poor speech quality in unsupervised acoustic unit representation learning for voice conversion by introducing a WaveNet auto-encoder with disentanglement methods, achieving top scores in naturalness (MOS 4.06) and intelligibility (CER 0.15) in the ZeroSpeech 2020 challenge.
Unsupervised representation learning of speech has been of keen interest in recent years, which is for example evident in the wide interest of the ZeroSpeech challenges. This work presents a new method for learning frame level representations based on WaveNet auto-encoders. Of particular interest in the ZeroSpeech Challenge 2019 were models with discrete latent variable such as the Vector Quantized Variational Auto-Encoder (VQVAE). However these models generate speech with relatively poor quality. In this work we aim to address this with two approaches: first WaveNet is used as the decoder and to generate waveform data directly from the latent representation; second, the low complexity of latent representations is improved with two alternative disentanglement learning methods, namely instance normalization and sliced vector quantization. The method was developed and tested in the context of the recent ZeroSpeech challenge 2020. The system output submitted to the challenge obtained the top position for naturalness (Mean Opinion Score 4.06), top position for intelligibility (Character Error Rate 0.15), and third position for the quality of the representation (ABX test score 12.5). These and further analysis in this paper illustrates that quality of the converted speech and the acoustic units representation can be well balanced.