Abstract
Dysarthria is a motor speech disability caused by weak muscles and organs involved in the articulation process, thereby affecting the speech intelligibility of individuals. Because this condition is linked to physical exhaustion disabilities, individuals not only have communication difficulties, but also have difficulty interacting with digital devices. Automatic speech recognition (ASR) makes an important difference for individuals with dysarthria since modern digital devices offer a better interaction medium that enables them to interact with their community and computers. Still, the performance of ASR technologies is poor in recognizing dysarthric speech, particularly for acute dysarthria. Multiple challenges, including dysarthric phoneme inaccuracy and labeling imperfection, are facing dysarthric ASR technologies. This paper proposes a spatio-temporal dysarthric ASR (DASR) system using Spatial Convolutional Neural Network (SCNN) and Multi-Head Attention Transformer (MHAT) to visually extract the speech features, and DASR learns the shapes of phonemes pronounced by dysarthric individuals. This visual DASR feature modeling eliminates phoneme-related challenges. The UA-Speech database is utilized in this paper, including different speakers with different speech intelligibility levels. However, because the proportion of usable speech data to the number of distinctive classes in the UA-speech database was small, the proposed DASR system leverages transfer learning to generate synthetic leverage and visuals. In benchmarking with other DASRs examined in this study, the proposed DASR system outperformed and improved the recognition accuracy for 20.72% of the UA-Speech database. The largest improvements were achieved for very-low (25.75%) and low intelligibility (33.67%).