A Textless Metric for Speech-to-Speech Comparison

• URL: http://arxiv.org/abs/2210.11835v2
• Date: Thu, 20 Jul 2023 11:56:40 GMT
• Title: A Textless Metric for Speech-to-Speech Comparison
• Authors: Laurent Besacier, Swen Ribeiro, Olivier Galibert, Ioan Calapodescu
• Abstract summary: We introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-the-art speech2unit encoders like HuBERT to convert speech utterances into discrete acoustic units.
• Score: 20.658229254191266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-the-art speech2unit encoders like HuBERT to convert speech utterances into discrete acoustic units. We then propose a simple and easily replicable neural architecture that learns a speech-based metric that closely corresponds to its text-based counterpart. This textless metric has numerous potential applications, including evaluating speech-to-speech translation for oral languages, languages without dependable ASR systems, or to avoid the need for ASR transcription altogether. This paper also shows that for speech-to-speech translation evaluation, ASR-BLEU (which consists in automatically transcribing both speech hypothesis and reference and compute sentence-level BLEU between transcripts) is a poor proxy to real text-BLEU even when ASR system is strong.

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