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Automatic speech recognition using psychoacoustic models.

E Zwicker, E Terhardt, E Paulus

    The Journal of the Acoustical Society of America
    |February 1, 1979
    PubMed
    Summary
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    This study presents a novel approach to automatic speech recognition using auditory parameters for preprocessing and digital computers for recognition. The system shows promise for recognizing continuous speech.

    Area of Science:

    • Speech Processing
    • Auditory Perception
    • Machine Learning

    Background:

    • Current automatic speech recognition (ASR) systems often require complex feature extraction.
    • Integrating psychoacoustic principles can potentially improve ASR robustness and efficiency.

    Purpose of the Study:

    • To develop and evaluate a novel ASR approach based on auditory parameters.
    • To implement a hybrid analog-digital system for speech preprocessing and recognition.

    Main Methods:

    • Analog hardware preprocessing using 24 bandpass filters to extract psychoacoustic parameters like loudness and roughness.
    • Digital computer-based recognition involving speech/silence discrimination, syllable peak detection, and boundary assumption.
    • Information flow reduction through dominant parameter selection based on psychoacoustic data.

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    Main Results:

    • The system successfully implemented key psychoacoustic principles for speech perception.
    • Initial results demonstrate a systematic and promising pathway for continuous speech recognition.
    • The hybrid analog-digital system architecture proved feasible for ASR.

    Conclusions:

    • The proposed approach offers a systematic and promising method for automatic speech recognition.
    • Psychoacoustic parameter extraction in analog hardware followed by digital recognition is a viable strategy.
    • Further development is needed to perfect the system for complete and accurate continuous speech recognition.