Detecting Stuttering Events in Transcripts of Children’s Speech View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017-09-27

AUTHORS

Sadeen Alharbi , Madina Hasan , Anthony J. H. Simons , Shelagh Brumfitt , Phil Green

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. More... »

PAGES

217-228

References to SciGraph publications

Book

TITLE

Statistical Language and Speech Processing

ISBN

978-3-319-68455-0
978-3-319-68456-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-68456-7_18

DOI

http://dx.doi.org/10.1007/978-3-319-68456-7_18

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091962646


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