Beyond Words: Towards Effective Modeling of Non-Verbal Vocalizations in ASR

Gene Yang; Haibin Wu; Peng Su; Ruizhe Huang; Suwon Shon; Bach Do; Minxue Niu; Zhaoheng Ni; Shang-Wen Li; Florian Metze; Yossi Adi; Ming Sun; Yuzong Liu

arXiv · 2607.01563

Beyond Words: Towards Effective Modeling of Non-Verbal Vocalizations in ASR

Gene Yang·Haibin Wu·Peng Su·Ruizhe Huang·Suwon Shon·Bach Do·Minxue Niu·Zhaoheng Ni·Shang-Wen Li·Florian Metze·Yossi Adi·Ming Sun·Yuzong Liu

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Abstract

Modern automatic speech recognition (ASR) systems excel at transcribing lexical content but often omit nonverbal vocalizations (NVs), such as laughter, breaths, coughs, and cries, that carry conversational and affective information. Modeling NVs in ASR is challenging because NV annotations are sparse and highly long-tailed, with frequent categories such as breaths and laughter dominating rarer events such as cries and coughs. We study three data-centric strategies for improving low-resource NV recognition: (1) a two-stage curriculum that first maps all NV events to a generic token and then fine-tunes on target categories; (2) inter-token transfer from high-resource events, such as laughter and breath, to rare events, such as crying; and (3) voice-conversion augmentation with class balancing. Experiments show that shared acoustic structure across vocal events can be exploited to improve rare-category detection while preserving lexical ASR quality.

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BibTeX RIS

Gene Yang, Haibin Wu, Peng Su, Ruizhe Huang, Suwon Shon, Bach Do, Minxue Niu, Zhaoheng Ni, Shang-Wen Li, Florian Metze, Yossi Adi, Ming Sun, Yuzong Liu. 2026-07-02. Beyond Words: Towards Effective Modeling of Non-Verbal Vocalizations in ASR. https://arxiv.org/abs/2607.01563

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