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Yunxin Zhao, Mili Kuruvilla-Dugdale, Minguang Song

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    This study developed personalized voices for individuals with Amyotrophic Lateral Sclerosis (ALS) using voice conversion. The novel approach significantly improved speech intelligibility and naturalness for ALS patients, restoring vocal identity.

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    Area of Science:

    • Speech processing
    • Biomedical engineering
    • Artificial intelligence

    Background:

    • Amyotrophic lateral sclerosis (ALS) causes progressive paralysis, leading to speech deterioration and reliance on generic electronic voices.
    • Restoring vocal identity for ALS patients is challenging due to limited premorbid speech data and varying dysarthria severity.

    Purpose of the Study:

    • To develop personalized voices for people with ALS using voice conversion.
    • To overcome challenges of limited dysarthric speech data and progressive fatigue.

    Main Methods:

    • Utilized healthy source speakers and a novel approach combining structured sparse spectral transform with linear regression-based frequency warping for spectral conversion.
    • Employed interpolation of transformed spectral frames for speech rate modification.
    • Conducted experiments with 4 healthy source speakers and 4 ALS target speakers.

    Main Results:

    • The proposed approach improved speech intelligibility by approximately 80% compared to the original ALS speech.
    • Converted voices were three times more similar to target ALS speakers than to source speakers.
    • Converted speech quality approached the "good" level on the MOS scale.

    Conclusions:

    • The developed voice conversion method offers a promising solution for restoring vocal identity in ALS patients.
    • This approach effectively addresses the challenges of limited data and disease progression in ALS speech synthesis.