Summary of Large Language Model Can Transcribe Speech in Multi-talker Scenarios with Versatile Instructions, by Lingwei Meng et al.
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions
by Lingwei Meng, Shujie Hu, Jiawen Kang, Zhaoqing Li, Yuejiao Wang, Wenxuan Wu, Xixin Wu, Xunying Liu, Helen Meng
First submitted to arxiv on: 13 Sep 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 Recent advancements in large language models (LLMs) have transformed various domains, yielding significant progress and new opportunities. This paper investigates the capability of LLMs in transcribing speech in multi-talker environments, a crucial challenge in automatic speech recognition (ASR). The authors propose MT-LLM, an approach that utilizes WavLM and Whisper encoder to extract multi-faceted speech representations sensitive to speaker characteristics and semantic context. These representations are then fine-tuned using LoRA-enabled LLMs for speech comprehension and transcription. Comprehensive experiments demonstrate the promising performance of MT-LLM in cocktail party scenarios, highlighting its potential to handle speech-related tasks based on user instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how well large language models can understand multiple people talking at once. Right now, these models are very good at understanding single speakers, but they struggle when many people are speaking together. The authors developed a new approach called MT-LLM that uses special codes to help the model better understand what each person is saying. They tested this approach and found it works really well in situations where multiple people are talking simultaneously. |
Keywords
» Artificial intelligence » Encoder » Lora
