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Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel: Presentation Slides

Hirschberg, Julia Bell; Biadsy, Fadi; Collins, Michael

In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similarities of phones between pairs of utterances. We employ this kernel to train SVM classifiers that estimate posterior probabilities, used during recognition. Testing our approach on four Arabic dialects from 30s cuts, we compare our performance to five approaches: PRLM; GMM-UBM; our own improved version of GMM-UBM which employs fMLLR adaptation; our recent discriminative phonotactic approach; and a state-of-the-art system: SDC-based GMM-UBM discriminatively trained. Our kernel-based technique outperforms all these previous approaches; the overall EER of our system is 4.9%.

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More About This Work

Academic Units
Computer Science
Publisher
Interspeech 2010
Published Here
August 5, 2013

Notes

Presentation paper is available at http://hdl.handle.net/10022/AC:P:21225

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