Unspeech: Unsupervised Speech Context Embeddings

TL;DR

This paper presents Unspeech embeddings, unsupervised context representations learned from large-scale speech data, improving speaker and speech recognition tasks without labeled data.

Contribution

Introduction of Unspeech embeddings trained via unsupervised learning on large speech datasets, enabling effective speech context features without transcriptions.

Findings

Unspeech embeddings outperform i-vector baselines in speaker comparison.

They reduce WER in out-of-domain speech recognition.

Source code and models are publicly released.

Abstract

We introduce "Unspeech" embeddings, which are based on unsupervised learning of context feature representations for spoken language. The embeddings were trained on up to 9500 hours of crawled English speech data without transcriptions or speaker information, by using a straightforward learning objective based on context and non-context discrimination with negative sampling. We use a Siamese convolutional neural network architecture to train Unspeech embeddings and evaluate them on speaker comparison, utterance clustering and as a context feature in TDNN-HMM acoustic models trained on TED-LIUM, comparing it to i-vector baselines. Particularly decoding out-of-domain speech data from the recently released Common Voice corpus shows consistent WER reductions. We release our source code and pre-trained Unspeech models under a permissive open source license.