Publications (3)4.63 Total impact
Conference Proceeding: Sparse Kernel Logistic Regression using Incremental Feature Selection for Text-Independent Speaker Identification[show abstract] [hide abstract]
ABSTRACT: Logistic regression is a well known classification method in the field of statistical learning. Recently, a kernelized version of logistic regression has become very popular, because it allows non-linear probabilistic classification and shows promising results on several benchmark problems. In this paper we show that kernel logistic regression (KLR) and especially its sparse extensions (SKLR) are useful alternatives to standard Gaussian mixture models (GMMs) and support vector machines (SVMs) in Speaker recognition. While the classification results of KLR and SKLR are similar to the results of SVMs, we show that SKLR produces highly sparse models. Unlike SVMs the kernel logistic regression also provides an estimate of the conditional probability of class membership. In speaker identification experiments the SKLR methods outperform the SVM and the GMM baseline system on the POLY-COST databaseSpeaker and Language Recognition Workshop, 2006. IEEE Odyssey 2006: The; 07/2006
Conference Proceeding: Limited Training Data Robust Speech Recognition Using Kernel-Based Acoustic Models[show abstract] [hide abstract]
ABSTRACT: Contemporary automatic speech recognition uses hidden-Markov-models (HMMs) to model the temporal structure of speech where one HMM is used for each phonetic unit. The states of the HMMs are associated with state-conditional probability density functions (PDFs) which are typically realized using mixtures of Gaussian PDFs (GMMs). Training of GMMs is error-prone especially if training data size is limited. This paper evaluates two new methods of modeling state-conditional PDFs using probabilistically interpreted support vector machines and kernel Fisher discriminants. Extensive experiments on the RMI (P. Price et al., 1988) corpus yield substantially improved recognition rates compared to traditional GMMs. Due to their generalization ability, our new methods reduce the word error rate by up to 13% using the complete training set and up to 33% when the training set size is reducedAcoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on; 06/2006 · 4.63 Impact Factor
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ABSTRACT: In this paper we use kernel-based Fisher Discriminants (KFD) for classification by integrating this method in a HMM-based speech recognition system. We translate the outputs of the KFD-classifier into conditional probabilities and use them as production probabilities of a HMM-based decoder for speech recognition. To obtain a good performance also in terms of computational complexity the Recursive Least Squares Algo-rithm (RLS-Algorithm) is enforced. We train and test the de-scribed hybrid structure on the Resource Management Corpus (RM1).