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arXiv · 2601.19767

Advanced Modeling of Interlanguage Speech Intelligibility Benefit with L1-L2 Multi-Task Learning Using Differentiable K-Means for Accent-Robust Discrete Token-Based ASR

Kentaro Onda·Satoru Fukayama·Daisuke Saito·Nobuaki Minematsu
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Abstract

Building ASR systems robust to foreign-accented speech is an important challenge in today's globalized world. A prior study explored the way to enhance the performance of phonetic token-based ASR on accented speech by reproducing the phenomenon known as interlanguage speech intelligibility benefit (ISIB), where foreign-accented speech is more intelligible to listeners sharing the speaker's native language than to native listeners. ISIB was technically implemented by using the speaker's L1 to learn k-means cluster centroids in an SSL feature space to obtain phonetic tokens. In this study, we propose a more advanced modeling of ISIB. By employing differentiable k-means and optimizing the entire module for both L1 and L2 ASR, the proposed method outperformed the baselines, both when using only native speech and when additionally incorporating a limited amount of accented speech. Notably, in the latter scenario, our method achieved approximately a 20% relative improvement in recognition accuracy.

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Kentaro Onda, Satoru Fukayama, Daisuke Saito, Nobuaki Minematsu. 2026-01-27. Advanced Modeling of Interlanguage Speech Intelligibility Benefit with L1-L2 Multi-Task Learning Using Differentiable K-Means for Accent-Robust Discrete Token-Based ASR. https://arxiv.org/abs/2601.19767

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