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Detecting Stuttering Events in Transcripts of Children’s Speech

  • Sadeen AlharbiEmail author
  • Madina Hasan
  • Anthony J. H. Simons
  • Shelagh Brumfitt
  • Phil Green
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10583)

Abstract

Stuttering is a common problem in childhood that may persist into adulthood if not treated in early stages. Techniques from spoken language understanding may be applied to provide automated diagnosis of stuttering from children speech. The main challenges however lie in the lack of training data and the high dimensionality of this data. This study investigates the applicability of machine learning approaches for detecting stuttering events in transcripts. Two machine learning approaches were applied, namely HELM and CRF. The performance of these two approaches are compared, and the effect of data augmentation is examined in both approaches. Experimental results show that CRF outperforms HELM by 2.2% in the baseline experiments. Data augmentation helps improve systems performance, especially for rarely available events. In addition to the annotated augmented data, this study also adds annotated human transcriptions from real stuttered children’s speech to help expand the research in this field.

Keywords

Stuttering event detection Speech disorder Human-computer interaction CRF HELM 

Notes

Acknowledgments

This research has been supported by the Saudi Ministry of Education, King Saud University.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sadeen Alharbi
    • 1
    Email author
  • Madina Hasan
    • 1
  • Anthony J. H. Simons
    • 1
  • Shelagh Brumfitt
    • 2
  • Phil Green
    • 1
  1. 1.Department of Computer ScienceThe University of SheffieldSheffieldUK
  2. 2.Department of Human Communication SciencesThe University of SheffieldSheffieldUK

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