Paper Info
Title
Dialect And Accent Recognition Using Phonetic-Segmentation Supervectors
Abstract
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-Southem English.
Year
Venue
Keywords
2011
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5
linguistics,information technology,computer science
Field
DocType
Citations 
Kernel (linear algebra),Indian English,Segmentation,Computer science,Support vector machine,Utterance,Speech recognition,Phone,Natural language processing,Artificial intelligence,Time complexity,Kernel (statistics)
Conference
13
PageRank 
References 
Authors
0.65
14
3
Name
Order
Citations
PageRank
Fadi Biadsy120715.14
Julia Hirschberg22982448.62
Daniel P. W. Ellis34198356.08