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An Automatic Prolongation Detection Approach in Continuous Speech With Robustness Against Speaking Rate Variations.

Iman Esmaili1, Nader Jafarnia Dabanloo1, Mansour Vali2

  • 1Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.

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Summary

This study introduces a novel method for automatically detecting speech prolongations in stuttering, aiding speech-language pathologists in diagnosis and treatment. The system achieves high accuracy and robustness across different speaking rates.

Keywords:
Attentionlanguagelearningpathologistsspeechspeech-language pathologystuttering

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Area of Science:

  • Speech Pathology
  • Computational Linguistics
  • Signal Processing

Background:

  • Automatic detection of speech prolongations is crucial for diagnosing and treating stuttering.
  • Current methods primarily focus on diagnosis, with less attention to treatment support for slower speech.
  • Speech-language pathologists (SLPs) require tools to assist in monitoring client progress during therapy.

Purpose of the Study:

  • To develop an automated method for detecting speech prolongations to support SLPs in stuttering diagnosis and treatment.
  • To provide a tool that helps monitor clients learning to speak more slowly during therapy sessions.

Main Methods:

  • Speech signals were processed using Perceptual Linear Predictive (PLP) features.
  • Correlation similarity measures were used to analyze successive speech frames.
  • Prolonged segments were identified when highly similar frames exceeded a speaking rate-dependent threshold.

Main Results:

  • The method achieved high detection accuracies of 99% on the UCLASS database and 97.1% on a Persian speech database.
  • The system demonstrated robustness against variations in speaking rate (70-130% of normal).
  • Performance was comparable to or better than three high-performance studies in automatic prolongation detection.

Conclusions:

  • The proposed method offers an effective and robust approach for automatic prolongation detection in stuttering.
  • This tool can significantly aid speech-language pathologists in both the diagnostic and therapeutic phases of stuttering intervention.
  • Further research can explore integration into real-time clinical feedback systems.