We describe a new approach to automatic dialect and accent recognition which exceeds state-of-the-art performance in three recognition tasks. This approach improves the accuracy and substantially lower the time complexity of our earlier phonetic-based kernel approach for dialect recognition. In contrast to state-of-the-art acoustic-based systems, our approach employs phone labels and segmentation to constrain the acoustic models. Given a speaker's utterance, we first obtain phone hypotheses using a phone recognizer and then extract GMM-supervectors for each phone type, effectively summarizing the speaker's phonetic characteristics in a single vector of phone-type supervectors. Using these vectors, we design a kernel function that computes the phonetic similarities between pairs of utterances to train SVM classifiers to identify dialects. Comparing this approach to the state-of-the-art, we obtain a 12.9% relative improvement in EER on Arabic dialects, and a 17.9% relative improvement for American vs. Indian English dialects. We also see a 53.5% relative improvement over a GMM-UBM on American Southern vs. Non-Southern English.
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