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• W307699962 abstract "Phonotactic language identification (LID) by means of n-gram statistics and discriminative classifiers is a popular approach for the LID problem. Low-dimensional representation of the n-gram statistics leads to the use of more diverse and efficient machine learning techniques in the LID. Recently, we proposed phototactic iVector as a low-dimensional representation of the n-gram statistics. In this work, an enhanced modeling of the n-gram probabilities along with regularized parameter estimation is proposed. The proposed model consistently improves the LID system performance over all conditions up to 15% relative to the previous state of the art system. The new model also alleviates memory requirement of the iVector extraction and helps to speed up subspace training. Results are presented in terms of Cavg over NIST LRE2009 evaluation set. Index Terms: Language identification, Subspace modeling, Subspace multinomial model" @default.
• W307699962 created "2016-06-24" @default.
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• W307699962 date "2013-08-25" @default.
• W307699962 modified "2023-09-26" @default.
• W307699962 title "Regularized subspace n-gram model for phonotactic ivector extraction" @default.
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• W307699962 doi "https://doi.org/10.21437/interspeech.2013-41" @default.
• W307699962 hasPublicationYear "2013" @default.
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