Summary of Voicebench: Benchmarking Llm-based Voice Assistants, by Yiming Chen et al.
VoiceBench: Benchmarking LLM-Based Voice Assistants
by Yiming Chen, Xianghu Yue, Chen Zhang, Xiaoxue Gao, Robby T. Tan, Haizhou Li
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
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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 paper introduces VoiceBench, a novel benchmark designed to evaluate large language model (LLM)-based voice assistants in real-world scenarios. Building on advancements like GPT-4o, which enable real-time speech interactions through LLM-based voice assistants, the authors highlight the limitations of current evaluations that focus primarily on automatic speech recognition or general knowledge evaluation with clean speeches. The proposed benchmark includes both real and synthetic spoken instructions that incorporate speaker characteristics, environmental factors, and content variations. Extensive experiments reveal the limitations of current models and offer valuable insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a special test for voice assistants that can understand and respond to speech. Right now, these assistants are mostly good at understanding clean speech with no background noise or weird voices. But in real life, people speak differently – they may have accents, be talking over background noise, or giving instructions. The authors of the paper want to make a test that checks how well voice assistants can handle all these different speaking styles and situations. They’re calling this test “VoiceBench”. By creating this test, the authors hope to help improve voice assistants so they can better understand us. |
Keywords
» Artificial intelligence » Gpt » Large language model
