*Equal Contributors
Parameter-efficient fine-tuning (PEFT) for personalizing automated speech recognition (ASR) has not too long ago proven promise for adapting basic inhabitants fashions to atypical speech. Nevertheless, these approaches assume a priori information of the atypical speech dysfunction being tailored for — the prognosis of which requires skilled information that isn’t all the time out there. Even given this information, knowledge shortage and excessive inter/intra-speaker variability additional restrict the effectiveness of conventional fine-tuning. To avoid these challenges, we first establish the minimal set of mannequin parameters required for ASR adaptation. Our evaluation of every particular person parameter’s impact on adaptation efficiency permits us to scale back Phrase Error Charge (WER) by half whereas adapting 0.03% of all weights. Assuaging the necessity for cohort-specific fashions, we subsequent suggest the novel use of a meta-learned hypernetwork to generate extremely individualized, utterance-level variations on-the-fly for a various set of atypical speech traits. Evaluating adaptation on the world, cohort and individual-level, we present that hypernetworks generalize higher to out-of-distribution audio system, whereas sustaining an total relative WER discount of 75.2% utilizing 0.1% of the complete parameter funds.