Towards Unsupervised Speech Recognition at the Syllable-Level
This addresses the problem of extending speech recognition to low-resource languages and non-parallel data, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles unsupervised speech recognition by proposing a syllable-level framework that avoids grapheme-to-phoneme converters and GAN instability, achieving up to a 40% relative reduction in character error rate on LibriSpeech and effective generalization to Mandarin.
Training speech recognizers with unpaired speech and text -- known as unsupervised speech recognition (UASR) -- is a crucial step toward extending ASR to low-resource languages in the long-tail distribution and enabling multimodal learning from non-parallel data. However, existing approaches based on phones often rely on costly resources such as grapheme-to-phoneme converters (G2Ps) and struggle to generalize to languages with ambiguous phoneme boundaries due to training instability. In this paper, we address both challenges by introducing a syllable-level UASR framework based on masked language modeling, which avoids the need for G2P and the instability of GAN-based methods. Our approach achieves up to a 40\% relative reduction in character error rate (CER) on LibriSpeech and generalizes effectively to Mandarin, a language that has remained particularly difficult for prior methods. Code will be released upon acceptance.