Unsupervised Speech Recognition at the Syllable Level

Liming Wang; Kai-Wei Chang; Kunio Kashino; David Harwath; Mark Hasegawa-Johnson; James R. Glass

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arXiv · 2608.22907

Unsupervised Speech Recognition at the Syllable Level

Liming Wang·Kai-Wei Chang·Kunio Kashino·David Harwath·Mark Hasegawa-Johnson·James R. Glass

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Abstract

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 low-resource languages that have remained particularly difficult for prior methods. Code is publicly available\footnote{https://github.com/cactuswiththoughts/SylCipher}.

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Liming Wang, Kai-Wei Chang, Kunio Kashino, David Harwath, Mark Hasegawa-Johnson, James R. Glass. 2026-08-24. Unsupervised Speech Recognition at the Syllable Level. https://arxiv.org/abs/2608.22907

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