Summary of Dmel: Speech Tokenization Made Simple, by He Bai et al.
dMel: Speech Tokenization made Simple
by He Bai, Tatiana Likhomanenko, Ruixiang Zhang, Zijin Gu, Zakaria Aldeneh, Navdeep Jaitly
First submitted to arxiv on: 22 Jul 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 This research paper introduces a novel approach to tokenizing continuous speech signals, enabling language modeling techniques to be applied to speech data. The authors investigate existing methods that either model semantic tokens or acoustic tokens, but these approaches have limitations. Instead, they propose discretizing mel-filterbank channels into discrete intensity bins, resulting in a simple representation called dMel. This new method outperforms existing tokenization methods on both automatic speech recognition (ASR) and text-to-speech synthesis (TTS) tasks within a unified framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand a foreign language without knowing what sounds represent certain words or phrases. That’s basically the challenge researchers faced when trying to apply language modeling techniques to speech data. Existing methods either focused on the meaning behind the words (semantic tokens) or the sounds themselves (acoustic tokens), but these approaches had some big drawbacks. Now, scientists have developed a new way to break down continuous speech into discrete chunks, called dMel, which outperforms other methods in recognizing spoken words and generating synthetic voices. |
Keywords
* Artificial intelligence * Tokenization
