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Showing 1 to 14 of 14 for “"Discriminative training"”.
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Investigations on discriminative training criteria
In this work, a framework for efficient discriminative training and modeling is developed and implemented for both small and large vocabulary continuous speech recognition. Special attention will be directed to the comparison and formalization of varying discriminative training criteria and …
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Discriminative training and acoustic modeling for automatic speech recognition
Discriminative training has become an important means for estimating model parameters in many statistical pattern recognition tasks. While standard learning methods based on the Maximum Likelihood criterion aim at optimizing model parameters only class individually, discriminative approaches …
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Discriminative training of hidden Markov Models for gesture recognition
… criterion and cannot perform as well as a discriminative classifier. This study employs minimum classification error training to produce a discriminative HMM classifier. The classifier is then applied to an isolated gesture recognition problem, using skeletal features. The Montalbano …
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Discriminative training of acoustic models in a segment-based speech recognizer
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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A log-linear discriminative modeling framework for speech recognition
… based on Gaussian hidden Markov models (HMMs).Discriminative techniques such as log-linear modeling have been investigated in speech recognition only recently. This thesis establishes a log-linear modeling framework in the context of discriminative training criteria, with examples from …
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Multi-level acoustic modeling for automatic speech recognition
… of the contexts, many units will not have enough training examples to train a robust model, resulting in a data sparsity problem. For nearly two decades, this data sparsity problem has been dealt with by a clustering-based framework which systematically groups different context-dependent units …
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Large-margin Gaussian mixture modeling for automatic speech recognition
Discriminative training for acoustic models has been widely studied to improve the performance of automatic speech recognition systems. To enhance the generalization ability of discriminatively trained models, a large-margin training framework has recently been proposed. This work investigates …
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Diskriminative Modellkombination in Spracherkennungssystemen mit großem Wortschatz
In this work, the theory of Discriminative Model Combination, DMC, is developed and implemented for large vocabulary continuous speech recognition. DMC is based on a discriminative training of the free parameters of distributions belonging to the exponential family. It is independent of the …
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Discriminative and adaptive training for robust speech recognition and understanding
… of deep learning. To achieve robust ASU, two discriminative training objectives are proposed for keyword spotting and topic classification: (1) To accurately recognize the semantically important keywords, the non-uniform error cost minimum classification error training of deep neural network …
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Modeling of image variability for recognition
… representing the variability.We also relate the discriminative maximum entropy approach to the Gaussian case and use the relationship to derive the novel maximum entropy linear discriminant analysis.Secondly, we investigate discrete deformation models – that map pixels onto pixels – of order …
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Semi-supervised learning for acoustic and prosodic modeling in speech applications
… of the thesis, we investigate semi-supervised training of Gaussian Mixtures Models (GMMs) and Hidden Markov Models (HMMs) which are the common probabilistic models of acoustic features in a state-of-the-art continuous density HMM based speech recognition system. Specifically, a family of …
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Towards the use of sub-band processing in automatic speaker recognition
… the genuine speaker model with every impostor’s training utterances, which requires considerably more computation than this new method which only needs one test per impostor.<br/><br/>Impostor ranking has potential applications in score normalisation for models which don’t use discriminative …
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Modeling spontaneous speech variability for large vocabulary continuous speech recognition
… relative frequencies of pronunciations in the training hypotheses. This standard maximum likelihood solution is compared to a novel discriminative training scheme which is an extension of the Discriminative Model Combination technique, proposed in [Beyerlein 01]. The developed iterative …
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Ensemble generation and compression for speech recognition
… to train a system directly toward a sequence discriminative criterion. Ensembles of these systems can exhibit highly diverse behaviours, because the systems are not biased toward any cross-entropy forced alignments. It is difficult to apply standard frame-level teacher-student learning with …