Classification of lexical stress patterns using deep neural network architecture

Lexical stress is a key diagnostic marker of disordered speech as it strongly affects speech perception. In this paper we introduce an automated method to classify between the different lexical stress patterns in children´s speech. A deep neural network is used to classify between strong-weak (SW), weak-strong (WS) and equal-stress (SS/WW) patterns in English by measuring the articulation change between the two successive syllables. The deep neural network architecture is trained using a set of acoustic features derived from pitch, duration and intensity measurements along with the energies in different frequency bands. We compared the performance of the deep neural classifier to a traditional single hidden layer MLP. Results show that the deep neural classifier outperforms the traditional MLP. The accuracy of the deep neural system is approximately 85% when classifying between the unequal stress patterns (SW/WS) and greater than 70% when classifying both equal and unequal stress patterns.