*Equal Contributors
Parameter-efficient fine-tuning (PEFT) for personalizing automated speech recognition (ASR) has lately proven promise for adapting normal inhabitants fashions to atypical speech. Nevertheless, these approaches assume a priori data of the atypical speech dysfunction being tailored for — the analysis of which requires skilled data that isn’t at all times accessible. Even given this information, knowledge shortage and excessive inter/intra-speaker variability additional restrict the effectiveness of conventional fine-tuning. To bypass these challenges, we first determine 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 cut 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 diversifications on-the-fly for a various set of atypical speech traits. Evaluating adaptation on the international, cohort and individual-level, we present that hypernetworks generalize higher to out-of-distribution audio system, whereas sustaining an general relative WER discount of 75.2% utilizing 0.1% of the complete parameter price range.