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Perception of Sound Waves01:01

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Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
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Sound Waves: Interference00:53

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Word-Final Phoneme Segmentation Using Cross-Correlation.

Emilian-Erman Mahmut1, Stelian Nicola1, Vasile Stoicu-Tivadar1

  • 1Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania.

Studies in Health Technology and Informatics
|November 23, 2020
PubMed
Summary

This study introduces an automated method for segmenting word-final speech sounds, improving screening for Speech Sound Disorders (SSD). The new approach aids Speech-Language Pathologists by providing accurate phoneme data for analysis.

Keywords:
Cross-correlationSSDaudio segmentation

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Area of Science:

  • Speech technology
  • Computational linguistics
  • Phonetics

Background:

  • Existing Speech Sound Disorder (SSD) screening tools often rely on manual analysis or rigid time-frame segmentation.
  • Current methods frequently use frequency-domain features, demanding significant computational resources and hindering real-time application.

Purpose of the Study:

  • To develop an automated word-final target phoneme segmentation method.
  • To create a computationally efficient pre-processing algorithm for SSD screening.

Main Methods:

  • The proposed method utilizes cross-correlation coefficients between reference and sample sound waves for segmentation.
  • A Python script automatically generates two .wav files for word-final phonemes from initial sound waves.

Main Results:

  • The algorithm successfully segments word-final phonemes, producing standardized .wav files.
  • These generated files serve as input for subsequent classification stages to identify mispronunciations.

Conclusions:

  • The automated segmentation method offers a more efficient and accurate approach to SSD screening.
  • This technique can assist Speech-Language Pathologists (SLPs) by providing reliable data for phoneme analysis and disorder identification.