Speech Vision: An End-to-End Deep Learning-Based Dysarthric Automatic Speech Recognition System

Dysarthria is a disorder that affects an individual’s speech intelligibility due to the paralysis of muscles and organs involved in articulation process. As condition often associated with physically debilitating disabilities, not only do such individuals face communication problems, but also interactions digital devices can become burden. For these individuals, automatic recognition (ASR) technologies make significant difference their lives as computing portable interaction medium, enabling them communicate others computers. However, ASR have performed poorly recognizing dysarthric speech, especially for severe dysarthria, multiple challenges facing systems. We identified are alternation inaccuracy phonemes, scarcity data, phoneme labeling imprecision. This paper reports on our second dysarthric-specific system, called Speech Vision (SV) tackles by adopting novel approach towards which features extracted visually, then SV learns see shape words pronounced individuals. visual acoustic modeling feature eliminates phoneme-related challenges. To address data problem, adopts augmentation techniques, generates synthetic visuals, leverages transfer learning. Benchmarking other state-of-the-art considered this study, outperformed improving accuracies 67% UA-Speech speakers, where biggest improvements were achieved dysarthria.