End-To-End Articulatory Modeling For Dysarthric Articulatory Attribute Detection

In this study, we focus on detecting articulatory attribute errors for dysarthric patients with cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS). There are two major challenges for this task. The pronunciation of dysarthric patients is unclear and inaccurate, which results in poor performances of traditional automatic speech recognition (ASR) systems and traditional automatic speech attribute transcription (ASAT). In addition, the data is limited because of the difficulty of recording. This study proposes an end-to-end automatic speech attribute transcription (E2E-ASAT) method for detecting articulatory attribute errors more precisely. To use the limited data more effectively, the parameters of the acoustic model are refactored into two layers and only one layer is retrained. Our proposed method showed good performances in both ASR and articulatory attribute detection. Our system has a potential as a rehabilitation tool.