Whisper-CD: Accurate Long-Form Speech Recognition using Multi-Negative Contrastive Decoding
Long-form speech recognition with large encoder-decoder models such as Whisper often exhibit hallucinations, repetition loops, and content omissions. These errors can accumulate and be further amplified when the previous segment's transcription is used as decoding context. We propose Whisper-CD, a training-free contrastive decoding framework that contrasts clean-audio logits against negative logits computed from three acoustically motivated perturbations: Gaussian noise injection, silence signal, and audio temporal shift. We aggregate these negatives via the log-sum-exp operator, building a unified multi-negative objective for token-by-token decoding. Across five English long-form benchma...
| Authors | Hoseong Ahn, Jeongyun Chae, Yoonji Park et al. |
|---|---|
| Primary category | cs.SD |
| Published | 6 March 2026 |
| Last updated at source | 22 June 2026 |
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