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Although personalized automatic speech recognition (ASR) models have recentlybeen designed to recognize even severely impaired speech, model performance maydegrade over time for persons with degenerating speech. The aims of this studywere to (1) analyze the change of performance of ASR over time in individualswith degrading speech, and (2) explore mitigation strategies to optimizerecognition throughout disease progression. Speech was recorded by fourindividuals with degrading speech due to amyotrophic lateral sclerosis (ALS).Word error rates (WER) across recording sessions were computed for three ASRmodels: Unadapted Speaker Independent (U-SI), Adapted Speaker Independent(A-SI), and Adapted Speaker Dependent (A-SD or personalized). The performanceof all three models degraded significantly over time as speech became moreimpaired, but the performance of the A-SD model improved markedly when it wasupdated with recordings from the severe stages of speech progression. Recordingadditional utterances early in the disease before speech degraded significantlydid not improve the performance of A-SD models. Overall, our findings emphasizethe importance of continuous recording (and model retraining) when providingpersonalized models for individuals with progressive speech impairments.

An analysis of degenerating speech due to progressive dysarthria on ASR performance