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arXiv Computation and Language By Saurabh Kumar, Diptiman Mohanta, Prasanta Kumar Ghosh

A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition

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The paper introduces a token‑level extension of Omni‑Temporal Classification (OTC) for automatic speech recognition, allowing unsupported tokens to be bypassed while preserving supervision for the rest of the word. Across 19 languages and three corpora, this token‑level OTC consistently outperforms standard CTC, achieving the lowest mean word error rate on every dataset and a 9.45% average relative WER reduction. A predictive‑entropy‑indexed schedule replaces epoch‑based relaxation, reducing training‑length dependence while maintaining performance.

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