Title: Unsupervised speech representation learning using WaveNet autoencoders,
Abstract: We consider the task of unsupervised extraction of meaningful latent
representations of speech by applying autoencoding neural networks to speech
waveforms. The goal is to learn a representation able to capture high level
semantic content from the signal, e.g. phoneme identities, while being
invariant to confounding low level details in the signal such as the underlying
pitch contour or background noise. The behavior of autoencoder models depends
on the kind of constraint that is applied to the latent representation. We
compare three variants: a simple dimensionality reduction bottleneck, a
Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE
(VQ-VAE). We analyze the quality of learned representations in terms of speaker
independence, the ability to predict phonetic content, and the ability to
accurately reconstruct individual spectrogram frames. Moreover, for discrete
encodings extracted using the VQ-VAE, we measure the ease of mapping them to
phonemes. We introduce a regularization scheme that forces the representations
to focus on the phonetic content of the utterance and report performance
comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic
unit discovery task.