Although personalized automatic speech recognition (ASR) models have recently
been designed to recognize even severely impaired speech, model performance may
degrade over time for persons with degenerating speech. The aims of this study
were to (1) analyze the change of performance of ASR over time in individuals
with degrading speech, and (2) explore mitigation strategies to optimize
recognition throughout disease progression. Speech was recorded by four
individuals with degrading speech due to amyotrophic lateral sclerosis (ALS).
Word error rates (WER) across recording sessions were computed for three ASR
models: Unadapted Speaker Independent (U-SI), Adapted Speaker Independent
(A-SI), and Adapted Speaker Dependent (A-SD or personalized). The performance
of all three models degraded significantly over time as speech became more
impaired, but the performance of the A-SD model improved markedly when it was
updated with recordings from the severe stages of speech progression. Recording
additional utterances early in the disease before speech degraded significantly
did not improve the performance of A-SD models. Overall, our findings emphasize
the importance of continuous recording (and model retraining) when providing
personalized models for individuals with progressive speech impairments.

本文分析了自动语音识别模型在变化的语音环境下的性能衰减问题和如何通过个性化模型、连续录音等方法实现优化识别的目的。