Asset learns to compress speech encoders
It uses reinforcement learning (RL) to adaptively compress audio input sequences for better performance in large language models (LLMs). The framework divides the encoded segments into variable-duration parts and then combines them into a continuous audio token, aiming to maintain task-oriented information while reducing the number of tokens needed. This approach has shown promising results in…
1 source primary source
Key points
- Introduces ASSET framework for adaptive speech compression
- Achieves comparable performance to oracle with fewer audio tokens
- Improves on phone-boundary oracle in Mandarin AISHELL
Read the original at arXiv cs.CL · by Cihan Xiao, Yuan Tseng, Matthew Wiesner, Desh Raj, Leibny Paola Garcia Perera, Sanjeev Khudanpur, David Harwath primary sourceOpen source ↗
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