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Related Experiment Videos

Segmenting speech using dynamic programming

J R Cohen

    The Journal of the Acoustical Society of America
    |May 1, 1981
    PubMed
    Summary
    This summary is machine-generated.

    This study models speech using Markov chains and dynamic programming to accurately segment speech signals. The novel algorithm achieves state-of-the-art performance, adapting to different speakers without bias.

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    Area of Science:

    • Speech processing
    • Computational linguistics
    • Machine learning

    Background:

    • Traditional speech segmentation methods often struggle with speaker variability.
    • Accurate speech segmentation is crucial for various downstream applications like speech recognition and analysis.

    Purpose of the Study:

    • To develop a novel, speaker-independent algorithm for speech segmentation.
    • To leverage probabilistic modeling and dynamic programming for improved segmentation accuracy.

    Main Methods:

    • Modeling speech signals as a Markov chain.
    • Developing a scoring mechanism to estimate segment boundary probabilities from speech observations.
    • Employing dynamic programming to compute the most probable speech segmentation.

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    Main Results:

    • The algorithm successfully converts speech signal observations into segment boundary probability estimates.
    • Dynamic programming yields a most-probable segmentation of the speech signal.
    • The method demonstrates state-of-the-art performance, independent of the speaker.

    Conclusions:

    • The proposed Markov chain and dynamic programming approach offers a robust and accurate method for speech segmentation.
    • The algorithm's ability to automatically adjust to speakers and incorporate prior information enhances its practical applicability.