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Title:Phonetic Landmark Detection for Automatic Language Identification
Author(s):Harwath, David
Contributor(s):Hasegawa-Johnson, Mark
Subject(s):language identification
language detection
phonetic landmark detection
speech processing
Abstract:This paper presents a method of augmenting shifted-delta cepstral coefficients (SDCCs) with the classification outputs of an array of support vector machines (SVMs) trained to detect a set of manner and place features on telephone speech. The SVM array allows for broad phoneme classification, and when this information is concatenated with SDCCs to form a hybrid feature vector for each acoustic frame, a set of Gaussian mixture models (GMMs) may be trained to perform automatic language identification ((LID). The NTIMIT telephone band speech corpus was used to train the SVM-based distinctive feature recognizer, while the NIST CallFriend telephone corpus was used for training and testing the rest of the system.
Issue Date:2010-05
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Date Available in IDEALS:2014-01-21

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