Papers with speech tokenization models
DM-Codec: Distilling Multimodal Representations for Speech Tokenization (2025.findings-emnlp)
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Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, Akmmahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
| Challenge: | Existing speech tokenization models lack contextual representations for speech synthesis . absence of contextual representation results in elevated WER and WIL scores . |
| Approach: | They propose a language model-guided distillation method that incorporates contextual information into a comprehensive speech tokenizer. |
| Outcome: | The proposed method outperforms state-of-the-art tokenization models in reducing WER and WIL scores. |