# Unspeech: Unsupervised Speech Context Embeddings

arXiv: https://arxiv.org/abs/1804.06775 · HTML: https://mlsanity.com/paper/1804.06775/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Self-Supervised Speech Representation Learning: A Review](https://mlsanity.com/paper/2205.10643/) (2022)
- [A Brief Overview of Unsupervised Neural Speech Representation Learning](https://mlsanity.com/paper/2203.01829/) (2022)
- [It's not what you said, it's how you said it: discriminative perception of speech as a multichannel communication system](https://mlsanity.com/paper/2105.00260/) (2021)
- [The Effect of Batch Size on Contrastive Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2402.13723/) (2024)
- [Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks](https://mlsanity.com/paper/2104.03899/) (2021)
- [Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition](https://mlsanity.com/paper/2410.02560/) (2024)

## Abstract (arXiv)

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.