Measuring the Redundancy of Decoder Layers in SpeechLLMs
Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters. We study how much of this decoder capacity is actually needed for speech tasks. Across two LLM families and three scales (1-8B), we show that decoder redundancy is largely inherited from the pretrained LLM: text and speech inputs yield similar redundant blocks. We then measure excess capacity by pruning decoder layers and analysing post-pruning healing to increase robustness. Our findings show that 7-8B models retain good ASR performance with only 60% of decoder layers, and the same trend extends to smaller scales with reduced pruning tolerance. W...
| Authors | Adel Moumen, Guangzhi Sun, Philip C Woodland |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 26 June 2026 |
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