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arXiv · 2412.12009

SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval

Yueqian Lin·Yuzhe Fu·Jingyang Zhang·Yudong Liu·Jianyi Zhang·Jingwei Sun·Hai "Helen" Li·Yiran Chen
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Abstract

We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models' ability to extract critical details from approximately 90-second spoken inputs. While current Speech LLMs excel at short-form tasks, they struggle with the computational and representational demands of longer audio sequences. To address this limitation, we propose SpeechPrune, a training-free token pruning strategy that uses speech-text similarity and approximated attention scores to efficiently discard irrelevant tokens. In SPIRAL, SpeechPrune achieves accuracy improvements of 29% and up to 47% over the original model and the random pruning model at a pruning rate of 20%, respectively. SpeechPrune can maintain network performance even at a pruning level of 80%. This approach highlights the potential of token-level pruning for efficient and scalable long-form speech understanding.

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Yueqian Lin, Yuzhe Fu, Jingyang Zhang, Yudong Liu, Jianyi Zhang, Jingwei Sun, Hai "Helen" Li, Yiran Chen. 2025-03-30. SpeechPrune: Context-aware Token Pruning for Speech Information Retrieval. https://arxiv.org/abs/2412.12009

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