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.
@article{arxiv.2210.11835,
title = {A Textless Metric for Speech-to-Speech Comparison},
author = {Laurent Besacier and Swen Ribeiro and Olivier Galibert and Ioan Calapodescu},
journal= {arXiv preprint arXiv:2210.11835},
year = {2023}
}
Comments
link to supplementary material: https://github.com/besacier/textless-metric