Lightweight LLM-based Speech Recognition via KAN Adapters

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In recent years, the combination of large language model (LLM) and pre-trained voice encoder has shown great potential in the field of automatic speech recognition (ASR). However, bridging the modal communication between acoustic characterization and language embedding often requires a large number of training parameters, which makes it difficult for them to apply in environments with limited resources. This study proposes to use the Kolmogorov-Arnold network (KANs) as a simplified adapter for the automatic speech recognition (ASR) system based on the Large Language Model (LLM). And by introducing a KAN adapter between the pre-trained voice encoder and TinyLlama-1.1B, the system improves the correspondence between acoustic characterization and language characterization with very few training parameters. The experimental results show stable optimization characteristics, with a word error rate (WER) of 16.79% and a character error rate (CER) of 10.46%. These results highlight the potential of KAN-based adapters in ASR systems with limited resources and parameters. The KAN-based adapter provides a promising and parameter-efficient solution for matching acoustic and language scenarios. In another words, in the resource-limited automatic speech recognition (ASR) scenario, which is crucial to computing efficiency and training stability, it shows significant advantages.