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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Discriminative Phonotactics for Dialect Recognition Using Context-Dependent Phone Classifiers: Presentation Slides Biadsy, Fadi; Soltau, Hagen; Mangu, Lidia. - : Odyssey 2010, The Speaker and Language Recognition Workshop, 2010 BASE Show details 2 Discriminative Phonotactics for Dialect Recognition Using Context-Dependent Phone Classifiers Soltau, Hagen; Hirschberg, Julia Bell; Biadsy, Fadi. - : Odyssey 2010, The Speaker and Language Recognition Workshop, 2010 BASE Show details 3 Discriminative Phonotactics for Dialect Recognition Using Context-Dependent Phone Classifiers ... Biadsy, Fadi; Soltau, Hagen; Mangu, Lidia. - : Columbia University, 2010 BASE Show details 4 Discriminative Phonotactics for Dialect Recognition Using Context-Dependent Phone Classifiers: Presentation Slides ... Biadsy, Fadi; Soltau, Hagen; Mangu, Lidia; Navratil, Jiri; Hirschberg, Julia Bell. - : Columbia University, 2010 Abstract: In this paper, we introduce a new approach to dialect recognition that relies on context-dependent (CD) phonetic differences between dialects as well as phonotactics. Given a speech utterance, we obtain the phone sequence using a CD-phone recognizer. We then identify the most likely dialect of these CD-phones using SVM classifiers. Augmenting these phones with the output of these classifiers, we extract augmented phonotactic features which are subsequently given to a logistic regression classifier to obtain a dialect detection score. We test our approach on the task of detecting four Arabic dialects from 30s utterances. We compare our performance to two baselines, PRLM and GMM-UBM, as well as to our own improved version of GMM-UBM which employs fMLLR adaptation. Our approach performs significantly better than all three baselines at 5% absolute Equal Error Rate (EER). The overall EER of our system is 6%. ... Keyword: Computer science; FOS Languages and literature; Information technology; Linguistics URL: https://dx.doi.org/10.7916/d84b38tx https://academiccommons.columbia.edu/doi/10.7916/D84B38TX BASE Hide details 5 Pseudo Pitch Synchronous analysis of speech with applications to speaker recognition Ramaswamy, Ganesh N.; Navrátil, Jiří; Kingsbury, Brian... In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 14 (2006) 2, 467-478 BLLDB OLC Linguistik Show details 6 Combining Cross-Stream And Time Dimensions In Phonetic Speaker Recognition Jin, Qin; Navratil, Jiri; Reynolds, Douglas A.... In: DTIC (2003) BASE Show details 7 Spoken language recognition - a step toward multilinguality in speech processing Navrátil, Jiří In: Institute of Electrical and Electronics Engineers. IEEE transactions on speech and audio processing. - New York, NY : Inst. 9 (2001) 6, 678-685 BLLDB Show details

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