Using SVM as back-end classifier for language identification
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Automatic Language Identification with Discriminative Language Characterization Based on SVM
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A target-oriented phonotactic front-end for spoken language recognition
IEEE Transactions on Audio, Speech, and Language Processing
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Integration of complementary honerecognizers for phonotactic language recognition
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Non-English response detection method for automated proficiency scoring system
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Language recognition with language total variability
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Maximum A Posteriori Linear Regression for language recognition
Expert Systems with Applications: An International Journal
A hierarchical language identification system for Indian languages
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Universal attribute characterization of spoken languages for automatic spoken language recognition
Computer Speech and Language
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We propose a novel approach to automatic spoken language identification (LID) based on vector space modeling (VSM). It is assumed that the overall sound characteristics of all spoken languages can be covered by a universal collection of acoustic units, which can be characterized by the acoustic segment models (ASMs). A spoken utterance is then decoded into a sequence of ASM units. The ASM framework furthers the idea of language-independent phone models for LID by introducing an unsupervised learning procedure to circumvent the need for phonetic transcription. Analogous to representing a text document as a term vector, we convert a spoken utterance into a feature vector with its attributes representing the co-occurrence statistics of the acoustic units. As such, we can build a vector space classifier for LID. The proposed VSM approach leads to a discriminative classifier backend, which is demonstrated to give superior performance over likelihood-based n-gram language modeling (LM) backend for long utterances. We evaluated the proposed VSM framework on 1996 and 2003 NIST Language Recognition Evaluation (LRE) databases, achieving an equal error rate (EER) of 2.75% and 4.02% in the 1996 and 2003 LRE 30-s tasks, respectively, which represents one of the best results reported on these popular tasks