arXiv:2606.19793v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision. Past research has demonstrated improved recognition through the use of hybrid DNN/HMM sequence discriminative training. This paper presents a comprehensive investigation of various combinations of acoustic features tailored to different Acoustic Models, offering suitable feature selections for each. The incorporation of Pitch features notably improved recognition performance, especially
The ongoing rapid advancements in AI, particularly in speech recognition and deep learning, are enabling more sophisticated and specialized applications.
Improving dysarthric speech recognition holds significant implications for accessibility, healthcare, and human-computer interaction for individuals with speech impairments.
New combinations of acoustic features and acoustic models are demonstrating improved accuracy in recognizing challenging speech patterns, potentially broadening the applicability of voice tech.
- · Assistive technology developers
- · Healthcare providers
- · Individuals with dysarthria
- · Voice AI companies
Enhanced accessibility and quality of life for individuals with speech impediments through more reliable communication tools.
Increased integration of voice control and AI assistants for people who were previously underserved by such technologies.
Potential for new therapies and diagnostic tools based on highly accurate dysarthric speech analysis.
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