Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning

• URL: http://arxiv.org/abs/2204.05148v2
• Date: Sat, 21 Oct 2023 10:15:36 GMT
• Title: Speech Sequence Embeddings using Nearest Neighbors Contrastive Learning
• Authors: Robin Algayres, Adel Nabli, Benoit Sagot, Emmanuel Dupoux
• Abstract summary: We introduce a simple neural encoder architecture that can be trained using an unsupervised contrastive learning objective. We show that when built on top of recent self-supervised audio representations, this method can be applied iteratively and yield competitive SSE.
• Score: 15.729812221628382
• License: http://creativecommons.org/licenses/by/4.0/
• 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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