Summary of Syllablelm: Learning Coarse Semantic Units For Speech Language Models, by Alan Baade et al.
SyllableLM: Learning Coarse Semantic Units for Speech Language Models
by Alan Baade, Puyuan Peng, David Harwath
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces a controllable self-supervised technique to merge speech representations into coarser syllable-like units, preserving semantic information. The method uses noisy boundaries extracted through analyzing correlations in pretrained encoder losses and iteratively improves model representations with a novel distillation technique. This produces controllable-rate semantic units at as low as 5Hz and 60bps, achieving state-of-the-art (SotA) performance in syllabic segmentation and clustering. Using these coarse tokens, the authors successfully train SyllableLM, a Speech Language Model (SpeechLM) that matches or outperforms current SotA SpeechLMs on various spoken language modeling tasks. SyllableLM also achieves significant improvements in efficiency with a 30x reduction in training compute and a 4x wall-clock inference speedup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make computers better at understanding speech. Right now, computers need lots of information about sounds to understand what people are saying. But this can be hard because sounds are very detailed and don’t always follow simple rules. The authors came up with a new way to group these sounds into bigger chunks that are easier for computers to work with. This lets them train special language models that are better at understanding speech than previous ones. These models can help us do things like recognize spoken words or even generate new speech. |
Keywords
» Artificial intelligence » Clustering » Distillation » Encoder » Inference » Language model » Self supervised
