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Conference Papers Year : 2022

Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning

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 [1, 2, 3], 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-byexample task on the LibriSpeech dataset to monitor future improvements in the field.
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Dates and versions

hal-03831888 , version 1 (27-10-2022)

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Robin Algayres, Adel Nabli, Benoît Sagot, Emmanuel Dupoux. Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning. Interspeech 2022 - 23rd INTERSPEECH Conference, Sep 2022, Incheon, South Korea. ⟨hal-03831888⟩
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