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Evaluative comparison of machine learning algorithms for stutter detection and classification.

Ramitha V1, Rhea Chainani1, Saharsh Mehrotra1

  • 1Department of Artificial Intelligence and Machine Learning, Symbiosis International University Symbiosis Institute of Technology, Pune, Maharashtra, India.

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|December 9, 2024
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Summary

This study introduces automatic stuttering detection to aid speech therapy and improve speech recognition for people who stutter (PWS). Machine learning models were compared on the SEP-28k dataset to classify stuttering types.

Keywords:
Automatic dysfluency detectionComparative analysisMachine learningSpeech disorderStutteringSupport Vector Classifier, Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression

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Area of Science:

  • Speech-Language Pathology
  • Computational Linguistics
  • Machine Learning

Background:

  • Stuttering is a neurodevelopmental speech disorder impacting social and professional life.
  • Automated detection of stuttering can support speech therapists and enhance speech recognition for people who stutter (PWS).

Purpose of the Study:

  • To conduct a comparative analysis of machine learning models for automatic stuttering event detection.
  • To classify five distinct types of dysfluencies: Prolongation, Interjection, Word Repetition, Sound Repetition, and Blocks.
  • To evaluate the influence of acoustic features on model performance and address class imbalance challenges.

Main Methods:

  • Utilized the SEP-28k dataset for model training and evaluation.
  • Performed comparative analysis across various machine learning models.
  • Investigated the impact of acoustic features on classification accuracy.

Main Results:

  • Comparative performance assessment of different machine learning models for stuttering detection.
  • Identification of key acoustic features contributing to accurate dysfluency classification.
  • Evaluation of model robustness in handling class imbalance within the dataset.

Conclusions:

  • Automated stuttering detection systems can effectively support speech-language pathologists.
  • Machine learning models show promise in improving speech recognition for people who stutter (PWS).
  • Further research into acoustic features and class imbalance is crucial for advancing stuttering detection technology.