An Automated Classification System Based on Regional Accent.

Identification of the native language from speech segment of a second language utterance, that is manifested as a distinct pattern of articulatory or prosodic behavior, is a challenging task. A method of classification of speakers, based on the regional English accent, is proposed in this paper. A database of English speech, spoken by the native speakers of three closely related Dravidian languages, was collected from a non-overlapping set of speakers, along with the native language speech data. Native speech samples from speakers of the regional languages of India, namely Kannada, Tamil, and Telugu are used for the training set. The testing set contains utterances of non-native English speakers of compatriots of the above three groups. Automatic identification of native language is proposed by using the spectral features of the non-native speech, that are classified using the classifiers such as Gaussian Mixture Models (GMM), GMM-Universal Background Model (GMM-UBM), and i-vector. Identification accuracy of 87.9 % was obtained using the GMM classifier, which was increased to 90.9 % by using the GMM-UBM method. But the i-vector-based approach gave a better accuracy of 93.9 % , along with EER of 6.1 % . The results obtained are encouraging, especially viewing the current state-of-the-art accuracies around 85 % . It is observed that the identification rate of nativity, while speaking English, is relatively higher at 95.2 % for the speakers of Kannada language, as compared to that for the speakers of Tamil or Telugu as their native language. [ABSTRACT FROM AUTHOR]