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Improving Text-Independent Forced Alignment to Support Speech-Language Pathologists with Phonetic Transcription.

Ying Li1, Bryce Johannas Wohlan1, Duc-Son Pham1

  • 1School of EECMS, Curtin University, Bentley, WA 6102, Australia.

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|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated phonetic transcription model for diagnosing speech sound disorders (SSDs). The novel approach improves accuracy and reduces bias in speech analysis, benefiting clinical applications.

Keywords:
forced alignmentphoneme segmentationphonological disordersspeech sound disordersspeech therapywav2vec 2.0

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Area of Science:

  • Speech Pathology
  • Computational Linguistics
  • Machine Learning

Background:

  • Phonetic transcription is vital for diagnosing speech sound disorders (SSDs).
  • Current forced alignment (FA) tools often require manual transcription and are prone to bias.
  • Limitations in existing FA tools hinder effective diagnosis and analysis of speech patterns.

Purpose of the Study:

  • To develop a novel, text-independent forced alignment model for automated phonetic transcription.
  • To address limitations of manual transcription and perceptual bias in diagnosing SSDs.
  • To improve the objectivity and efficiency of speech sound disorder assessment.

Main Methods:

  • Utilized a pre-trained wav2vec 2.0 model for automatic speech segmentation and recognition.
  • Employed an unsupervised segmentation tool (UnsupSeg) for accurate phoneme boundary identification.
  • Implemented nearest-neighbor classification, connectionist temporal classification (CTC), and post-processing for enhanced segmentation.

Main Results:

  • The model achieved competitive performance, with a harmonic mean score of 76.88% on the TIMIT dataset (normal speakers).
  • For the first time, the model was evaluated on the TORGO dataset (SSD speakers), achieving a harmonic mean score of 70.31%.
  • Demonstrated effective performance on speakers with speech sound disorders, a key advancement.

Conclusions:

  • The developed model offers a significant advancement in the objective and less biased assessment of SSDs.
  • Its effectiveness with SSD speakers opens new research and clinical applications in speech pathology.
  • This automated approach has the potential to revolutionize speech disorder diagnosis and treatment.