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    Area of Science:

    • Speech synthesis
    • Pathological speech
    • Machine learning

    Background:

    • Pathological speech synthesis presents challenges in controlling speech severity.
    • Existing text-to-speech (TTS) systems struggle with generating diverse pathological speech characteristics.
    • Generating multi-severity datasets for single-speaker pathological speech is difficult.

    Purpose of the Study:

    • To develop a novel pathological text-to-speech (TTS) synthesis system capable of controlling speech severity.
    • To improve the naturalness, controllability, and stability of synthesized pathological speech.
    • To leverage existing speaker embeddings (x-vectors) for severity conditioning.

    Main Methods:

    • Utilized data augmentation to create a single-speaker multi-severity training dataset.
    • Employed x-vectors as a conditioning variable for speech severity control.
    • Modified the GradTTS architecture to improve duration modeling for pathological speech.

    Main Results:

    • The proposed GradTTS system demonstrated superior performance compared to the baseline TransformerTTS.
    • Objective and subjective evaluations confirmed the system's effectiveness.
    • The system successfully generated more natural, controllable, and stable pathological speech samples.

    Conclusions:

    • The developed GradTTS system offers effective control over pathological speech severity.
    • The integration of x-vectors and architectural modifications enhances pathological TTS capabilities.
    • This work advances the field of controllable and natural-sounding pathological speech synthesis.