Modelling Accents for Automatic Speech Recognition

Accent is cited as an issue for speech recognition systems. If they are to be widely deployed, Automatic Speech Recognition (ASR) systems must deliver consistently high performance across user populations. Hence the development of accentrobust ASR is of significant importance. This research investigates techniques for compensating for the effects of accents on performance of Hidden Markov Model (HMM) based ASR systems. Recently, HMM systems based on Deep Neural Networks (DNNs) have achieved superior performance to more traditional systems based on Gaussian Mixture Models (GMMs), due to the discriminative nature of DNNs. Our research confirms, this by showing that a DNN system outperforms the GMM system even after an accent-dependent acoustic model was selected using Accent Identification (AID), followed by speaker adaptation. The average performance of the DNN system over all accent groups is maximized when either accent diversity is highest, or data from “difficult” accent-groups is included in the training set.