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51
Using SelfOrganizing Maps and Learning Vector Quantization for Mixture Density Hidden Markov Models
, 1997
"... This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the col ..."
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Cited by 20 (8 self)
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This work presents experiments to recognize pattern sequences using hidden Markov models (HMMs). The pattern sequences in the experiments are computed from speech signals and the recognition task is to decode the corresponding phoneme sequences. The training of the HMMs of the phonemes using the collected speech samples is a difficult task because of the natural variation in the speech. Two neural computing paradigms, the SelfOrganizing Map (SOM) and the Learning Vector Quantization (LVQ) are used in the experiments to improve the recognition performance of the models. A HMM consists of sequential states which are trained to model the feature changes in the signal produced during the modeled process. The output densities applied in this work are mixtures of Gaussian density functions. SOMs are applied to initialize and train the mixtures to give a smooth and faithful presentation of the feature vector space defined by the corresponding training samples. The SOM maps similar feature vect...
Startsynchronous search for large vocabulary continuous speech recognition
 IEEE Trans. Speech and Audio Processing
"... Abstract — In this paper, we present a novel, efficient search strategy for large vocabulary continuous speech recognition. The search algorithm, based on a stack decoder framework, utilizes phonelevel posterior probability estimates (produced by a connectionist/hidden Markov model acoustic model) ..."
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Abstract — In this paper, we present a novel, efficient search strategy for large vocabulary continuous speech recognition. The search algorithm, based on a stack decoder framework, utilizes phonelevel posterior probability estimates (produced by a connectionist/hidden Markov model acoustic model) as a basis for phone deactivation pruning—a highly efficient method of reducing the required computation. The singlepass algorithm is naturally factored into the timeasynchronous processing of the word sequence and the timesynchronous processing of the hidden Markov model state sequence. This enables the search to be decoupled from the language model while still maintaining the computational benefits of timesynchronous processing. The incorporation of the language model in the search is discussed and computationally cheap approximations to the full language model are introduced. Experiments were performed on the North American Business News task using a 60 000 word vocabulary and a trigram language model. Results indicate that the computational cost of the search may be reduced by more than a factor of 40 with a relative search error of less than 2 % using the techniques discussed in the paper. Index Terms — Hidden Markov model, large vocabulary continuous speech recognition, phone deactivation pruning, search, stack decoding. I.
Discriminative Training of Hidden Markov Models
, 1998
"... vi Abbreviations vii Notation viii 1 Introduction 1 2 Hidden Markov Models 4 2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 HMM Modelling Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 HMM Topology . . . . . . . . . ..."
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Cited by 20 (0 self)
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vi Abbreviations vii Notation viii 1 Introduction 1 2 Hidden Markov Models 4 2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 HMM Modelling Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 HMM Topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Finding the Best Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Setting the Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Objective Functions 19 3.1 Properties of Maximum Likelihood Estimators . . . . . . . . . . . . . . . . . . . 19 3.2 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3 Maximum Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Frame Discrimination . . . . . . . . . . . . . . . . ....
OnLine HandPrinting Recognition with Neural Networks
, 1996
"... The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in online word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of handprinted English text. The central concept of ..."
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Cited by 14 (0 self)
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The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in online word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of handprinted English text. The central concept of a neural net as a character classifier provides a good base for a recognition system; longstanding issues relative to training, generalization, segmentation, probabilistic formalisms, etc., need to resolved, however, to get excellent performance. A number of innovations in how to use a neural net as a classifier in a word recognizer are presented: negative training, stroke warping, balancing, normalized output error, error emphasis, multiple representations, quantized weights, and integrated word segmentation all contribute to efficient and robust performance.
Hidden Neural Networks: A Framework For HMM/NN Hybrids
 In Proceedings ICASSP97, April 2124
, 1997
"... This paper presents a general framework for hybrids of Hidden Markov models (HMM) and neural networks (NN). In the new framework called Hidden Neural Networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is normal ..."
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Cited by 12 (5 self)
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This paper presents a general framework for hybrids of Hidden Markov models (HMM) and neural networks (NN). In the new framework called Hidden Neural Networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is normalized globally as opposed to the local normalization enforced on parameters in standard HMMs. Furthermore, all parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood (CML) criterion. The HNNs show clear performance gains compared to standard HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task. 1. INTRODUCTION Among speech research scientists it is widely believed that HMMs are one of the best and most successful modelling...
LARGE VOCABULARY CONTINUOUS SPEECH RECOGNITION WITH CONTEXTDEPENDENT DBNHMMS
"... The contextindependent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a contextdependent DBNHMM system that dramatically outperforms strong Gaussian mixture model (GMM)HMM baselines o ..."
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Cited by 11 (5 self)
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The contextindependent deep belief network (DBN) hidden Markov model (HMM) hybrid architecture has recently achieved promising results for phone recognition. In this work, we propose a contextdependent DBNHMM system that dramatically outperforms strong Gaussian mixture model (GMM)HMM baselines on a challenging, large vocabulary, spontaneous speech recognition dataset from the Bing mobile voice search task. Our system achieves absolute sentence accuracy improvements of 5.8 % and 9.2 % over GMMHMMs trained using the minimum phone error rate (MPE) and maximum likelihood (ML) criteria, respectively, which translate to relative error reductions of 16.0 % and 23.2%. Index Terms — Speech recognition, deep belief network, contextdependent phone, LVCSR, DBNHMM 1.
Effective Training of a Neural Network Character Classifier for Word Recognition
, 1997
"... Brandyn Webb The Future 4578 Fieldgate Rd. ..."
A System for the OffLine Recognition of Handwritten Text
 in ICPR'94, IEEE
, 1994
"... A new system for the recognition of handwritten text is described. The system goes from raw, binary scanned images of census forms to ASCII transcriptions of the fields contained within the forms. The first step is to locate and extract the handwritten input from the forms. Then, a large number of c ..."
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Cited by 7 (1 self)
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A new system for the recognition of handwritten text is described. The system goes from raw, binary scanned images of census forms to ASCII transcriptions of the fields contained within the forms. The first step is to locate and extract the handwritten input from the forms. Then, a large number of character subimages are extracted and individually classified using a MLP (MultiLayer Perceptron) . A Viterbilike algorithm is used to assemble the individual classified character subimages into optimal interpretations of an input string, taking into account both the quality of the overall segmentation and the degree to which each character subimage of the segmentation matches a character model. The system uses two different statistical language models, one based on a phrase dictionary and the other based on a simple word grammar. Hypotheses from recognition based on each language model are integrated using a decision tree classifier. Results from the application of the system to the recogn...
Hidden Neural Networks
 NEURAL COMPUTATION
, 1997
"... A general framework for hybrids of Hidden Markov models (HMMs) and neural networks (NNs) called Hidden Neural Networks (HNNs) is described. The paper begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN the usual HMM probability par ..."
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Cited by 7 (3 self)
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A general framework for hybrids of Hidden Markov models (HMMs) and neural networks (NNs) called Hidden Neural Networks (HNNs) is described. The paper begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN the usual HMM probability parameters are replaced by the outputs of state specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.