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.
@article{arxiv.2510.03639,
title = {Towards Unsupervised Speech Recognition at the Syllable-Level},
author = {Liming Wang and Junrui Ni and Kai-Wei Chang and Saurabhchand Bhati and David Harwath and Mark Hasegawa-Johnson and James R. Glass},
journal= {arXiv preprint arXiv:2510.03639},
year = {2025}
}