Acoustic model selection for recognition of regional accented speech

Accent is cited as an issue for speech recognition systems [1]. Research has shown that accent mismatch between the training and the test data will result in significant accuracy reduction in Automatic Speech Recognition (ASR) systems. Using HMM based ASR trained on a standard English accent, our study shows that the error rates can be up to seven times higher for accented speech, than for standard English. Hence the development of accent-robust ASR systems is of significant importance. This research investigates different acoustic modelling techniques for compensating for the effects of regional accents on the performance of ASR systems. The study includes conventional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and more contemporary Deep Neural Network (DNN)-HMM systems. In both cases we consider both supervised and unsupervised techniques. This work uses the WSJCAM0 corpus as a set of ‘accent neutral’ data and accented data from the Accents of the British Isles (ABI) corpora. Initially, we investigated a model selection approach, based on automatic accent identification (AID). Three AID systems were developed and evaluated in this work, namely i-vector, phonotactic, and ACCDIST-SVM. Each focuses on a different property of speech to achieve AID. We use two-dimensional projections based on Expectation Maximization-Principal Component Analysis (EM-PCA) and Linear Discriminative Analysis (LDA) to visualise the different accent spaces and use these visualisations to analyse the AID and ASR results. In GMM-HMM based ASR systems, we show that using a small amount of data from a test speaker to select an accented acoustic model using AID, results in superior performance compared to that obtained with unsupervised or supervised speaker adaptation. A possible objection to AID-based model selection is that in each accent there exist speakers who have varying degrees of accent, or whose accent exhibits properties of other accents. This motived us to investigated whether using an acoustic model created based on neighbouring speakers in the accent space can result in better performance. In conclusion, the maximum reduction in error rate achieved over all GMM-HMM based adaptation approaches is obtained by using AID to select an accent-specifc model followed by speaker adaptation. It is also shown that the accuracy of an AID system does not have a high impact on the gain obtained by accent