Automatic intonation classification using temporal patterns in utterance-level pitch contour and perceptually motivated pitch transformation.

Abstract

Second language learners of British English (BE) are typically trained for four intonation classes: Glide-up, Glide-down, Dive, and Take-off. Automatic four-way intonation classification could be useful to evaluate a learner's pronunciation. However, such automatic classification is challenging without having manually annotated tones, typically considered in intonation analysis and classification tasks. In this, a three-dimensional feature sequence is proposed representing temporal patterns in the utterance-level f0 contour using a perceptually motivated pitch transformation. Hidden Markov model based classification experiments conducted using a training material for teaching BE intonation demonstrate the benefit of the proposed approach over the baseline scheme considered.