Overcoming Decoder Inconsistencies in Whisper for Dravidian and Low-Resource Languages
Multilingual ASR models such as Whisper perform well on high-resource languages but exhibit substantially higher Word Error Rates (WER) for Dravidian languages compared to Indo-Aryan ones. Through linguistic and dataset analysis, we show that Dravidian languages have longer words, higher vocabulary diversity, and lower repetition, resulting in sparse token distributions and frequent character-level substitution errors. Baseline fine-tuning further reveals decoder imbalance between self-attention (linguistic context) and cross-attention (acoustic cues). Although synthetic token-repetition experiments indicate potential gains, they are impractical. Motivated by these observations, we introd...
| Authors | Chowdam Venkata Kumar, Kumud Tripathi, Pankaj Wasnik |
|---|---|
| Primary category | cs.CL |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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