Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Discriminative training"”.
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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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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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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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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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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 …