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|April 30, 2021

Summary

This study introduces Speech Vision (SV), a novel automatic speech recognition (ASR) system for individuals with dysarthria. SV improves speech recognition accuracy by visually analyzing speech, overcoming challenges like phoneme inaccuracies and data scarcity.

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Area of Science:

Computer Science
Speech Technology
Assistive Technology

Background:

Dysarthria impairs speech intelligibility, creating communication barriers and hindering digital device interaction.
Existing automatic speech recognition (ASR) systems struggle with dysarthric speech due to phoneme variations, limited data, and labeling inaccuracies.
Effective ASR for dysarthria can significantly enhance communication and digital accessibility for affected individuals.

Purpose of the Study:

To develop a dysarthric-specific ASR system that overcomes limitations of current technologies.
To improve the accuracy and usability of ASR for individuals with dysarthria, particularly those with severe impairments.

Main Methods:

Introduced Speech Vision (SV), a novel ASR system utilizing visual acoustic modeling to analyze speech features.
Addressed data scarcity using visual data augmentation, synthetic data generation, and transfer learning.
Benchmarked SV against state-of-the-art dysarthric ASR systems.

Main Results:

SV demonstrated superior performance, improving recognition accuracy for 67% of speakers in the UA-Speech dataset.
Significant improvements were observed for individuals with severe dysarthria.
The visual acoustic modeling approach effectively mitigated phoneme-related challenges.

Conclusions:

Speech Vision (SV) represents a significant advancement in ASR for dysarthric speech.
The novel visual approach and data augmentation strategies enhance ASR performance for this population.
SV has the potential to greatly improve communication and digital interaction for individuals with dysarthria.