Beyond WER: Probing Whisper's Sub-token Decoder Across Diverse Language Resource Levels

• URL: http://arxiv.org/abs/2509.25516v1
• Date: Mon, 29 Sep 2025 21:20:05 GMT
• Title: Beyond WER: Probing Whisper's Sub-token Decoder Across Diverse Language Resource Levels
• Authors: Siyu Liang, Nicolas Ballier, Gina-Anne Levow, Richard Wright,
• Abstract summary: This paper introduces a fine-grained analysis of Whisper's multilingual decoder.<n>Our method traces the beam search path, capturing sub-token guesses and their associated probabilities.<n>Lower resource languages fare worse on these metrics, but also exhibit distinct clustering patterns in sub-token usage.
• Score: 6.627057618324123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While large multilingual automatic speech recognition (ASR) models achieve remarkable performance, the internal mechanisms of the end-to-end pipeline, particularly concerning fairness and efficacy across languages, remain underexplored. This paper introduces a fine-grained analysis of Whisper's multilingual decoder, examining its sub-token hypotheses during transcription across languages with various resource levels. Our method traces the beam search path, capturing sub-token guesses and their associated probabilities. Results reveal that higher resource languages benefit from higher likelihood of the correct token being top-ranked, greater confidence, lower predictive entropy, and more diverse alternative candidates. Lower resource languages fare worse on these metrics, but also exhibit distinct clustering patterns in sub-token usage sometimes influenced by typology in our PCA and t-SNE analysis. This sub-token probing uncovers systematic decoding disparities masked by aggregate error rates and points towards targeted interventions to ameliorate the imbalanced development of speech technology.

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