  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.
