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arXiv · 2510.22172

M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR

Ruixiang Mao·Xiangnan Ma·Qing Yang·Ziming Zhu·Yucheng Qiao·Yuan Ge·Tong Xiao·Shengxiang Gao·Zhengtao Yu·Jingbo Zhu
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Abstract

The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanism creates a smooth and monotonic mapping from acoustic features to target tokens, achieving performance on Mandarin competitive with other NAR approaches. However, without finer-grained guidance, its stability degrades in some languages such as English and French. In this paper, we propose Multi-scale CIF (M-CIF), which performs multi-level alignment by integrating character and phoneme level supervision progressively distilled into subword representations, thereby enhancing robust acoustic-text alignment. Experiments show that M-CIF reduces WER compared to the Paraformer baseline, especially on CommonVoice by 4.21% in German and 3.05% in French. To further investigate these gains, we define phonetic confusion errors (PE) and space-related segmentation errors (SE) as evaluation metrics. Analysis of these metrics across different M-CIF settings reveals that the phoneme and character layers are essential for enhancing progressive CIF alignment.

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Ruixiang Mao, Xiangnan Ma, Qing Yang, Ziming Zhu, Yucheng Qiao, Yuan Ge, Tong Xiao, Shengxiang Gao, Zhengtao Yu, Jingbo Zhu. 2025-10-25. M-CIF: Multi-Scale Alignment For CIF-Based Non-Autoregressive ASR. https://arxiv.org/abs/2510.22172

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