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

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.

Sound (cs.SD); Computation and Language (cs.CL)

arXiv:2510.22172 [cs.SD]

(or arXiv:2510.22172v2 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2510.22172 Focus to learn more arXiv-issued DOI via DataCite

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