Probabilistic Latent Variable Models (LVMs) provide an alternative to
self-supervised learning approaches for linguistic representation learning from
speech. LVMs admit an intuitive probabilistic interpretation where the latent
structure shapes the information extracted from the signal. Even though LVMs
have recently seen a renewed interest due to the introduction of Variational
Autoencoders (VAEs), their use for speech representation learning remains
largely unexplored. In this work, we propose Convolutional Deep Markov Model
(ConvDMM), a Gaussian state-space model with non-linear emission and transition
functions modelled by deep neural networks. This unsupervised model is trained
using black box variational inference. A deep convolutional neural network is
used as an inference network for structured variational approximation. When
trained on a large scale speech dataset (LibriSpeech), ConvDMM produces
features that significantly outperform multiple self-supervised feature
extracting methods on linear phone classification and recognition on the Wall
Street Journal dataset. Furthermore, we found that ConvDMM complements
self-supervised methods like Wav2Vec and PASE, improving on the results
achieved with any of the methods alone. Lastly, we find that ConvDMM features
enable learning better phone recognizers than any other features in an extreme
low-resource regime with few labeled training examples.

本文提出了 ConvDMM，这是一种使用非线性发射和转移函数模型的高斯状态空间模型，并使用深度卷积神经网络作为结构变分近似的推理网络的无监督模型。当在大规模语音数据集上进行训练时，ConvDMM 产生的特征在线性电话分类和在 WSJ 数据集上的识别方面显着优于多个自我监督的特征提取方法，并且可以与 Wav2Vec 和 PASE 等自我监督的方法相辅相成，进一步提高了结果。最后，我们发现，在少量标记训练示例的极低资源之下，ConvDMM 功能使得学习更好的电话识别器比任何其他功能。