We Augmented Whisper With kNN and You Won't Believe What Came Next

• URL: http://arxiv.org/abs/2410.18850v1
• Date: Thu, 24 Oct 2024 15:32:52 GMT
• Title: We Augmented Whisper With kNN and You Won't Believe What Came Next
• Authors: Maya K. Nachesa, Vlad Niculae,
• Abstract summary: We show that Whisper, a transformer end-to-end speech model, benefits from $k$NN. We discuss implications for speaker adaptation, and analyze improvements by gender, accent, and age.
• Score: 10.174848090916669
• License:
• Abstract: Speech recognition performance varies by language, domain, and speaker characteristics such as accent, and fine-tuning a model on any of these categories may lead to catastrophic forgetting. $k$ nearest neighbor search ($k$NN), first proposed for neural sequence decoders for natural language generation (NLG) and machine translation (MT), is a non-parametric method that can instead adapt by building an external datastore that can then be searched during inference time, without training the underlying model. We show that Whisper, a transformer end-to-end speech model, benefits from $k$NN. We investigate the differences between the speech and text setups. We discuss implications for speaker adaptation, and analyze improvements by gender, accent, and age.

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