Word based dialect classification using extreme learning machines
Muhammad Rizwan, Babafemi O. Odelowo, David V. Anderson · 2016
It is well known that the variability in speech caused by the accents or dialects of speakers degrades the performance of speech recognition systems. One method to prevent this degradation is to correctly identify the accent or dialect of a speaker so that the putative system can be designed to use this information. In this paper, we apply the extreme learning machine, an efficient neural network learning algorithm, to the problem of accent/dialect classification on the TIMIT dataset. Mel frequency cepstrum coefficients (MFCCs) and the normalized energy parameter are computed from word samples in each dialect class, and these along with their first and second derivatives are used as training features. A novel architecture and a weighting scheme are used to classify North American dialects into seven groups. Using this technique, we obtain an classification accuracy of 76.92%, which to our knowledge is the best result reported for dialect classification on the TIMIT dataset.