Noisy channel adaptation in language identification | IEEE Conference Publication | IEEE Xplore

Noisy channel adaptation in language identification


Abstract:

Language identification (LID) of speech data recorded over noisy communication channels is a challenging problem especially when the LID system is tested on speech data f...Show More

Abstract:

Language identification (LID) of speech data recorded over noisy communication channels is a challenging problem especially when the LID system is tested on speech data from an unseen communication channel (not seen in training). In this paper, we consider the scenario in which a small amount of adaptation data is available from a new communication channel. Various approaches are investigated for efficient utilization of the adaptation data in a supervised as well as unsupervised setting. In a supervised adaptation framework, we show that support vector machines (SVMs) with higher order polynomial kernels (HO-SVM) trained using lower dimensional representations of the the Gaussian mixture model supervectors (GSVs) provide significant performance improvements over the baseline SVM-GSV system. In these LID experiments, we obtain 30% reduction in error-rate with 6 hours of adaptation data for a new channel. For unsupervised adaptation, we develop an iterative procedure for re-labeling the development data using a co-training framework. In these experiments, we obtain considerable improvements(relative improvements of 13 %) over a self-training framework with the HO-SVM models.
Date of Conference: 02-05 December 2012
Date Added to IEEE Xplore: 31 January 2013
ISBN Information:
Conference Location: Miami, FL, USA

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