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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Improving the Efficiency of Dysarthria Voice Conversion System Based on Data Augmentation.

Wei-Zhong Zheng, Ji-Yan Han, Chen-Yu Chen

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |November 8, 2023
    PubMed
    Summary

    This study introduces Dysarthria Voice Conversion 3.1 (DVC 3.1), a data augmentation system improving speech intelligibility for dysarthria patients. DVC 3.1 significantly enhances communication by reducing recording burdens and boosting clarity.

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    Area of Science:

    • Speech and Hearing Sciences
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Dysarthria, a neurological speech disorder, impairs vocal muscle control, leading to unclear speech and communication difficulties.
    • Existing voice conversion (VC) methods for dysarthria often require extensive speech data from both patients and target speakers, posing a significant recording burden.
    • There is a need for efficient VC systems that minimize data requirements while effectively improving speech intelligibility.

    Purpose of the Study:

    • To propose a novel data augmentation-based voice conversion (VC) system, termed Dysarthria Voice Conversion 3.1 (DVC 3.1), to alleviate the recording burden for dysarthria patients.
    • To enhance the speech intelligibility of individuals with dysarthria through synthesized speech data.
    • To evaluate the effectiveness of DVC 3.1 compared to a baseline system (DVC 3.0) and unprocessed dysarthria speech.

    Main Methods:

    • Developed DVC 3.1 utilizing a data augmentation approach, combining text-to-speech (TTS) synthesis and the StarGAN-VC architecture.
    • Synthesized a comprehensive corpus of target-like and patient-like speech data to reduce the need for extensive recordings.
    • Employed Google automatic speech recognition (Google ASR) for objective evaluation and conducted listening tests for subjective assessment of speech intelligibility.

    Main Results:

    • DVC 3.1 significantly improved Google ASR performance for two dysarthria patients, showing enhancements of approximately [62.4%, 43.3%] and [55.9%, 57.3%] over unprocessed speech and DVC 3.0, respectively.
    • Subjective evaluations indicated substantial increases in speech intelligibility with DVC 3.1, approximately [54.2%, 22.3%] and [63.4%, 70.1%] compared to unprocessed speech and DVC 3.0.
    • The data augmentation strategy in DVC 3.1 effectively synthesized necessary speech data, reducing recording demands.

    Conclusions:

    • The proposed DVC 3.1 system demonstrates significant potential for improving speech intelligibility in dysarthria patients.
    • DVC 3.1 enhances verbal communication quality by overcoming the limitations of traditional VC methods requiring large datasets.
    • This data augmentation-driven approach offers a practical and effective solution for dysarthria speech rehabilitation.