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Related Experiment Video

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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Multiple Vowels Repair Based on Pitch Extraction and Line Spectrum Pair Feature for Voice Disorder.

Tao Zhang, Yangyang Shao, Yaqin Wu

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    This study introduces a novel method for pathological voice repair, successfully restoring multiple vowels beyond single vowel repair. The technique significantly improves voice quality metrics for disordered speech.

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    Area of Science:

    • Speech processing and biomedical engineering.
    • Focuses on acoustic analysis and signal processing for voice disorders.

    Background:

    • Voice disorders affect various populations, necessitating effective pathological voice repair.
    • Existing methods are limited to single vowel repair (/a/), facing challenges with multiple vowels due to pitch instability and formant reconstruction issues.

    Purpose of the Study:

    • To propose and validate a novel method for multiple pathological voice repair.
    • To extend voice repair capabilities from single vowels to multiple vowels (/a/, /i/, /u/).
    • To improve the accuracy of pathological voice classification and the quality of voice reconstruction.

    Main Methods:

    • Developed a multiple vowels repair method utilizing pitch extraction and Line Spectrum Pair (LSP) features.
    • Employed Wavelet Transform and Hilbert-Huang Transform for accurate pitch extraction.
    • Utilized LSP features for robust formant reconstruction, followed by synthesis of pitch and formant for repaired voice generation.
    • Incorporated deep neural networks for voice recognition and classification of normal versus pathological voices.

    Main Results:

    • Successfully repaired multiple vowels (/a/, /i/, /u/), broadening the scope of pathological voice repair.
    • Achieved significant improvements in key performance metrics: 45.87% in Segmental Signal-to-Noise Ratio, 50.37% in LSP distance measure, and 15.56% in Mel cepstral distance measure.
    • Spectrogram analysis confirmed a prominent and effective voice repair outcome.

    Conclusions:

    • The proposed method offers a successful approach to pathological voice repair for multiple vowels.
    • The integration of advanced signal processing techniques (Wavelet Transform, Hilbert-Huang Transform, LSP) enhances voice reconstruction quality.
    • This research advances the field of voice disorder treatment by providing a more comprehensive and effective repair solution.