Automatic assessment of pronunciation quality of children within assisted speech therapy

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.