Summary of Dm-codec: Distilling Multimodal Representations For Speech Tokenization, by Md Mubtasim Ahasan et al.
DM-Codec: Distilling Multimodal Representations for Speech Tokenization
by Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, A K M Mahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DM-Codec architecture, a novel distillation approach combining language and self-supervised speech models, significantly outperforms state-of-the-art speech tokenization models. By incorporating contextual information and multimodal representations (acoustic, semantic, and contextual), the DM-Codec reduces Word Error Rate by up to 13.46%, Word Information Lost by 9.82%, and improves speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset. The proposed approach addresses limitations in existing speech representations, which typically overlook the crucial role of contextual representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make computers understand speech better. Currently, there are two main ways to do this: one based on how audio sounds and another based on what words mean. However, these approaches don’t take into account the context in which someone is speaking, like who they are talking to or where they are. The researchers found that without considering context, computers make more mistakes when transcribing speech. To fix this, they developed a new method called DM-Codec that combines all three types of information (audio, words, and context). This approach significantly improves how well computers can understand speech. |
Keywords
» Artificial intelligence » Distillation » Self supervised » Tokenization
