  Disordered speech recognition profound implications for improving the quality
of life for individuals afflicted with, for example, dysarthria. Dysarthric
speech recognition encounters challenges including limited data, substantial
dissimilarities between dysarthric and non-dysarthric speakers, and significant
speaker variations stemming from the disorder. This paper introduces
Perceiver-Prompt, a method for speaker adaptation that utilizes P-Tuning on the
Whisper large-scale model. We first fine-tune Whisper using LoRA and then
integrate a trainable Perceiver to generate fixed-length speaker prompts from
variable-length inputs, to improve model recognition of Chinese dysarthric
speech. Experimental results from our Chinese dysarthric speech dataset
demonstrate consistent improvements in recognition performance with
Perceiver-Prompt. Relative reduction up to 13.04% in CER is obtained over the
fine-tuned Whisper.
