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An adaptive algorithm for the automatic segmentation of continuous stuttered speech

W E Lucas1, W Hudson

  • 1Department of Electrical and Computer Engineering, Kansas State University, Manhattan 66506.

Biomedical Sciences Instrumentation
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

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This study introduces an adaptive algorithm for automatic speech segmentation into voiced, unvoiced, and silence regions. This method aids in identifying stuttered speech patterns by analyzing speech segments and handling repetitions.

Area of Science:

  • Speech Processing
  • Computational Linguistics
  • Bioacoustics

Background:

  • Automatic speech segmentation is crucial for analyzing speech patterns.
  • Existing methods may struggle with the complexities of stuttered speech, including prolongations and silent intervals.
  • Accurate segmentation is key to understanding speech disorders.

Purpose of the Study:

  • To develop and evaluate an adaptive algorithm for automatic speech segmentation into voiced, unvoiced, and silence (V/U/S) regions.
  • To enable the reliable identification of stuttered speech through precise segmentation.
  • To ensure the algorithm effectively handles speech disfluencies like prolongations and repetitions.

Main Methods:

  • Utilizing a digital filter bank with parameters derived from formant frequencies of voiced speech segments.

Related Experiment Videos

  • Implementing an adaptive algorithm for V/U/S segmentation of continuous speech.
  • Designing specific handling for audible and silent prolongations and repetitions in stuttered speech.

    • Main Results:

    • The algorithm successfully segments continuous speech into distinct voiced, unvoiced, and silence regions.
    • The segmentation approach facilitates the identification of syllable-like structures.
    • Experimental results demonstrate the system's reliability in segmenting both fluent and stuttered speech from male and female subjects.

    Conclusions:

    • The proposed adaptive algorithm provides reliable automatic speech segmentation.
    • This method is effective for analyzing stuttered speech by accurately segmenting disfluencies.
    • The V/U/S segmentation approach offers a robust foundation for speech disorder analysis.