Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning

Robin Algayres; Adel Nabli; Benoit Sagot; Emmanuel Dupoux

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arXiv · 2204.05148

Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning

Robin Algayres·Adel Nabli·Benoit Sagot·Emmanuel Dupoux

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Abstract

We introduce a simple neural encoder architecture that can be trained using an unsupervised contrastive learning objective which gets its positive samples from data-augmented k-Nearest Neighbors search. We show that when built on top of recent self-supervised audio representations, this method can be applied iteratively and yield competitive SSE as evaluated on two tasks: query-by-example of random sequences of speech, and spoken term discovery. On both tasks our method pushes the state-of-the-art by a significant margin across 5 different languages. Finally, we establish a benchmark on a query-by-example task on the LibriSpeech dataset to monitor future improvements in the field.

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Robin Algayres, Adel Nabli, Benoit Sagot, Emmanuel Dupoux. 2022-04-11. Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning. https://arxiv.org/abs/2204.05148

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