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    Summary
    This summary is machine-generated.

    This study introduces a new Encoder-Decoder Generative Adversarial Network (E-DGAN) for pathological to normal voice conversion. The E-DGAN method effectively improves speech intelligibility and personalization for multiple pathological voice types.

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    Area of Science:

    • Speech processing
    • Artificial intelligence
    • Medical technology

    Background:

    • Increasing prevalence of voice-related diseases necessitates advanced pathological voice conversion methods.
    • Current methods are limited, often converting only a single type of pathological voice.
    • Need for personalized and intelligible speech conversion for individuals with voice disorders.

    Purpose of the Study:

    • To propose a novel Encoder-Decoder Generative Adversarial Network (E-DGAN) for pathological to normal voice conversion.
    • To develop a method capable of handling multiple types of pathological voices.
    • To enhance speech intelligibility and personalization for pathological voices.

    Main Methods:

    • Utilized mel filter banks for feature extraction.
    • Employed an encoder-decoder conversion network to transform pathological to normal voice mel spectrograms.
    • Integrated a residual conversion network and a neural vocoder for personalized speech synthesis.
    • Introduced a "content similarity" metric for subjective evaluation.

    Main Results:

    • Achieved an 18.67% increase in pathological voice intelligibility.
    • Demonstrated a 2.60% improvement in content similarity.
    • Successfully personalized pathological voice conversion for 20 different speakers.
    • Outperformed five other pathological voice conversion methods in evaluations.

    Conclusions:

    • The proposed E-DGAN method significantly improves pathological voice intelligibility and personalization.
    • The method is effective for multiple pathological voice types and diverse speakers.
    • The developed "content similarity" metric aids in evaluating conversion quality.