Abstract
In this paper we present our results in automatic evaluation of pronunciation quality of children with dyslalia (mispronunciation of specific phonemes). Our aim is to offer real-time, quality feedback so that to reduce the gap between human assisted and artificial speech therapy. We present both theoretical and practical related issues such as: acquisition of data, human scoring, Hidden Markov Models training and classification, and the performances of our system. The obtained results encourage us to continue the development of Logomon – the first computer based speech therapy system for Romanian language. Ill. 1, bibl. 17, tabl. 2 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.122.6.1813
Highlights
Assistive speech therapy is a general term that includes assistive, adaptive, and rehabilitative techniques for people with pronunciation disorders
One of the most important and provocative characteristics of a Computer Based Speech Therapy System (CBST) is to provide real time feedback – an ability traditionally reserved for humans (i.e. Speech and Language Therapists – SLT)
Together with emotion recognition, automatic assessment of pronunciation quality is considered to be the key for reducing the gap between traditional and computer assisted speech therapy [1]
Summary
Assistive speech therapy is a general term that includes assistive, adaptive, and rehabilitative techniques for people with pronunciation disorders. Our researches on assisted therapy of speech disorders has been started since 2005, when we designed Logomon – the first CBST for Romanian language. In addition with the utilisation of the software without the presence of a SLT – portability, there is another main advantage of this approach – objectivity (i.e. the opportunity to obtain scores that are not influenced by the subjectivity). Together, these benefits justify the investments in intelligent interfaces that are able to perform real time analysis of speech production and to react
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