In order to efficiently transmit and store speech signals, speech codecs
create a minimally redundant representation of the input signal which is then
decoded at the receiver with the best possible perceptual quality. In this work
we demonstrate that a neural network architecture based on VQ-VAE with a
WaveNet decoder can be used to perform very low bit-rate speech coding with
high reconstruction quality. A prosody-transparent and speaker-independent
model trained on the LibriSpeech corpus coding audio at 1.6 kbps exhibits
perceptual quality which is around halfway between the MELP codec at 2.4 kbps
and AMR-WB codec at 23.05 kbps. In addition, when training on high-quality
recorded speech with the test speaker included in the training set, a model
coding speech at 1.6 kbps produces output of similar perceptual quality to that
generated by AMR-WB at 23.05 kbps.

本文展示了使用基于 VQ-VAE 和 WaveNet 解码器的神经网络架构进行低码率语音编码的有效性，该模型在 LibriSpeech 语音数据集上产生了 1.6 kbps 的编码音频，其感知质量在 2.4 kbps 的 MELP 编解码器和 23.05 kbps 的 AMR-WB 编解码器之间，并且在高质量声音上进行训练后，该模型产生的音频质量与 23.05 kbps 的 AMR-WB 编解码器相当。

基于 VQ-VAE 和 WaveNet 解码器的低比特率语音编码

Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder

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. Since the learned representation is tuned to
contain only phonetic content, we resort to using a high capacity WaveNet
decoder to infer information discarded by the encoder from previous samples.
Moreover, 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.

本研究旨在通过自编码神经网络从语音波形中提取有意义的潜在表示，并比较三个变体：简单维度约束、高斯变分自编码器和离散量化矢量自编码器，成果表明此方法在 ZeroSpeech 2017 任务中获得了可比拟的表现。