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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis (ALS)
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Classification of speech dysfluencies using LPC based parameterization techniques.

M Hariharan1, Lim Sin Chee, Ooi Chia Ai

  • 1School of Mechatronic Engineering, UniMAP, Perlis, Malaysia. hari@unimap.edu.my

Journal of Medical Systems
|January 21, 2011
PubMed
Summary

This study compared Linear Predictive Coefficients (LPC), LPCC, and WLPCC for stuttering detection. WLPCC slightly outperformed other methods in identifying stuttered speech events.

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Area of Science:

  • Speech processing
  • Biomedical engineering
  • Computational linguistics

Background:

  • Stuttering detection is crucial for speech therapy and research.
  • Accurate feature extraction is key to reliable stuttering event identification.
  • Existing methods require robust feature sets for classification.

Purpose of the Study:

  • To compare Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for stuttered event recognition.
  • To evaluate the effectiveness of these features using k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) classifiers.
  • To investigate the impact of parameter tuning and statistical normalization on classification accuracy.

Main Methods:

  • Feature extraction using LPC, LPCC, and WLPCC on manually segmented stuttered speech samples from the UCLASS database.
  • Classification of speech dysfluencies using kNN and LDA.
  • Analysis of parameter variations including frame length, overlap, pre-emphasis, and feature order.
  • Application of statistical normalization prior to feature extraction.

Main Results:

  • LPC, LPCC, and WLPCC features are effective for identifying stuttered events.
  • Weighted Linear Prediction Cepstral Coefficients (WLPCC) demonstrated slightly superior performance compared to LPCC and LPC.
  • Statistical normalization significantly improved speech dysfluencies classification accuracy.
  • Parameter tuning (frame length, overlap, pre-emphasis, order) impacts performance.

Conclusions:

  • WLPCC is a promising feature for stuttered speech recognition.
  • Statistical normalization is a beneficial preprocessing step for enhancing stuttering detection accuracy.
  • The choice of feature extraction method and classifier parameters influences the reliability of stuttering event identification.