Download or read book Hierarchical Connectionist Acoustic Modeling for Domain Adaptive Large Vocabulary Speech Recognition written by Jürgen Fritsch and published by . This book was released on 2000 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Syllable Articulatory feature and Stress accent Model of Speech Recognition written by Shuangyu Chang and published by . This book was released on 2002 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi level Acoustic Modeling for Automatic Speech Recognition written by Hung-An Chang (Ph. D.) and published by . This book was released on 2012 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Context-dependent acoustic modeling is commonly used in large-vocabulary Automatic Speech Recognition (ASR) systems as a way to model coarticulatory variations that occur during speech production. Typically, the local phoneme context is used as a means to define context-dependent units. Because the number of possible context-dependent units can grow exponentially with the length 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 into clusters such that each cluster can have enough data. Although dealing with the data sparsity issue, the clustering-based approach also makes all context-dependent units within a cluster have the same acoustic score, resulting in a quantization effect that can potentially limit the performance of the context-dependent model. In this work, a multi-level acoustic modeling framework is proposed to address both the data sparsity problem and the quantization effect. Under the multi-level framework, each context-dependent unit is associated with classifiers that target multiple levels of contextual resolution, and the outputs of the classifiers are linearly combined for scoring during recognition. By choosing the classifiers judiciously, both the data sparsity problem and the quantization effect can be dealt with. The proposed multi-level framework can also be integrated into existing large-vocabulary ASR systems, such as FST-based ASR systems, and is compatible with state-of-the-art error reduction techniques for ASR systems, such as discriminative training methods. Multiple sets of experiments have been conducted to compare the performance of the clustering-based acoustic model and the proposed multi-level model. In a phonetic recognition experiment on TIMIT, the multi-level model has about 8% relative improvement in terms of phone error rate, showing that the multi-level framework can help improve phonetic prediction accuracy. In a large-vocabulary transcription task, combining the proposed multi-level modeling framework with discriminative training can provide more than 20% relative improvement over a clustering baseline model in terms of Word Error Rate (WER), showing that the multi-level framework can be integrated into existing large-vocabulary decoding frameworks and that it combines well with discriminative training methods. In speaker adaptive transcription task, the multi-level model has about 14% relative WER improvement, showing that the proposed framework can adapt better to new speakers, and potentially to new environments than the conventional clustering-based approach.

Download or read book Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition written by Joel Praveen Pinto and published by . This book was released on 2010 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Connectionist Speech Recognition written by Hervé A. Bourlard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous speech recognition systems based on hidden Markov models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e. HMM emission probability estimation and feature extraction. The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems. Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.

Download or read book Statistical Optimization of Acoustic Models for Large Vocabulary Speech Recognition written by Rusheng Hu and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation investigates optimization of acoustic models in speech recognition. Two new optimization methods are proposed for phonetic decision tree (PDT) search and Hidden Markov modeling (HMM)-- the knowledge-based adaptive PDT algorithm and the HMM gradient boosting algorithm. Investigations are conducted to applying both methods to improve word error rate of the state-of-the-art speech recognition system. However, these two methods are developed in a general machine learning background and their applications are not limited to speech recognition. The HMM gradient boosting method is based on a function approximation scheme from the perspective of optimization in function space rather than the parameter space, based on the fact that the Gaussian mixture model in each HMM state is an additive model of homogeneous functions (Gaussians). It provides a new scheme which can jointly optimize model structure and parameters. Experiments are conducted on the World Street Journal (WSJ) task and good improvements on word error rate are observed. The knowledge-based adaptive PDT algorithm is developed under a trend toward knowledge-based systems and aims at optimizing the mapping from contextual phones to articulatory states by maximizing implicit usage of the phonological and phonetic information, which is presumed to be contained in large data corpus. A computational efficient algorithm is developed to incorporate this prior knowledge in PDT construction. This algorithm is evaluated on the Telehealth conversational speech recognition and significant improvement on system performance is achieved.

Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.

Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Download or read book Improvement of Decoding Engine Phonetic Decision Tree in Acoustic Modeling for Online Large Vocabulary Conversational Speech Recognition written by Jian Xue and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, new approaches are proposed for online large vocabulary conversational speech recognition, including a fast confusion network algorithm, novel features and a Random Forests based classifier for word confidence annotation, new improvements in speech decoding speed and latency, novel lookahead phonetic decision tree state tying and Random Forests of phonetic decision tree state tying for acoustic modeling of speech sound units. The fast confusion network algorithm significantly improves the time complexity from O(T3) to O(T), with T equaling the number of links in a word lattice. Several novel features, as well as Random Forests based classification technique are proposed to improve word annotation accuracy for automatic captioning. In order to improve the speed of speech decoding engine, we propose to use complementary word confidence scores to prune uncompetitive search paths, and use subspace distribution clustering hidden Markov modeling to speed up computation of acoustic scores and local confidence scores. We further integrate pre-backtrace in decoding search to significantly reduce captioning latency. In this work we also investigate novel approaches to improve the performance of phonetic decision tree state tying, including two lookahead methods and a Random Forests method. Constrained lookahead method finds an optimal question among n pre-selected questions for each split node to decrease effects of outliers, and it also discounts the contributions of likelihood gains by deeper decedents. Stochastic full lookahead method uses sub-tree size instead of likelihood gain as a measure for phonetic question selection, in order to produce small trees with better generalization capability and consistent with training data. The Random Forests method uses an ensemble of phonetic decision trees to derive a single strong model for each speech unit. We investigate several methods of combining the acoustic scores from multiple models obtained from multiple phonetic decision trees in decoding search. We further propose clustering methods to compact the Random Forests generated acoustic models to speed up decoding search.

Download or read book Subphonetic Acoustic Modeling for Speaker independent Continuous Speech Recognition written by and published by . This book was released on 1993 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: To model the acoustics of a large vocabulary well while staying within a reasonable memory capacity, most speech recognition systems use phonetic models to share parameters across different words in the vocabulary. This dissertation investigates the merits of modeling at the subphonetic level. We demonstrate that sharing parameters at the subphonetic level provides more accurate acoustic models than sharing at the phonetic level. The concept of subphonetic parameter sharing can be applied to any class of parametric models. Since the first-order hidden Markov model (HMM) has been overwhelmingly successful in speech recognition, this dissertation bases all its studies and experiments on HMMs. The subphonetic unit we investigate is the state of phonetic HMMs. We develop a system in which similar Markov states of phonetic models share the same Markov parameters. The shared parameter (i.e., the output distribution) associated with a cluster of similar states is called a senone because of its state dependency. The phonetic models that share senones are shared-distribution models or SDMs. Experiments show that SDMs offer more accurate acoustic models than the generalized-triphone model presented by Lee. Senones are next applied to offer accurate models for triphones not experienced in the system training data. In this dissertation, two approaches for modeling unseen triphones are studied - purely decision-tree based senones and a hybrid approach using the concept of Markov state quantization. Both approaches indeed offer a significant error reduction over the previously accepted approach of monophone model substitution. However, the purely decision-tree based senone approach is preferred for its simplicity.

Download or read book High Accuracy Large Vocabulary Speech Recognition Using Mixture Tying and Consistency Modeling written by and published by . This book was released on 1994 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of mixture tying for a given amount of training data and computational resources. Experimental results on the Wall Street Journal (WSJ) Corpus show that this new form of output distribution achieves a 25% reduction in error rate over typical tied- mixture systems. We then show that an additional improvement can be achieved by modeling local time correlation with linear discriminant features.

Download or read book Fast Speaker adaptive Training for Large vocabulary Speech Recognition written by Ming-Whei Feng and published by . This book was released on 1989 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Acoustic Modeling and Front end Design for Distant Speech Recognition written by Seyedmahdad Mirsamadi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a significant increase in the popularity of voice-enabled technologies which use human speech as the primary interface with machines. Recent advancements in acoustic modeling and feature design have increased the accuracy of Automatic Speech Recognition (ASR) to levels that enable voice interfaces to be used in many applications. However, much of the current performance is dependent on the use of close-talking microphones, (i.e., scenarios in which the user speaks directly into a hand-held or body-worn microphone). There is still a rather large performance gap experienced in distant-talking scenarios in which speech is recorded by far-field microphones that are placed at a distance from the speaker. In such scenarios, the distorting effects of distance (such as room reverberation and environment noise) make the recognition task significantly more challenging. In this dissertation, we propose novel approaches for designing a distant-talking ASR front-end as well as training robust acoustic models to reduce the existing gap between far-field and close-talking ASR performance. Specifically, we i) propose a novel multi-channel front-end enhancement algorithm for improved ASR in reverberant rooms using distributed non-uniform microphone arrays with random unknown locations; ii) propose a novel neural network model training approach using adversarial training to improve the robustness of multi-condition acoustic models that are trained directly on far-field data; iii) study alternate neural network adaptation strategies for far-field adaptation to the acoustic properties of specific target environments. Experimental results are provided based on far-field benchmark tasks and datasets which demonstrate the effectiveness of the proposed approaches for increasing far-field robustness in ASR. Based on experiments using reverberated TIMIT sentences, the proposed multi-channel front-end provides WER improvements of +21.5% and +37.7% in two-channel and four-channel scenarios over a single-channel scenario in which the channel with best signal quality is selected. On the acoustic modeling side and based on results of experiments on AMI corpus, the proposed multi-domain training approach provides a relative character error rate reduction of +3.3% with respect to a conventional multi-condition trained baseline, and +25.4% with respect to a clean-trained baseline.

Download or read book Adaptive Vocabularies in Large Vocabulary Conversational Speech Recognition written by Petra Geutner and published by . This book was released on 2000 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Study on Acoustic Modeling and Adaptation in Hmm Based Speech Recognition written by Bin Ma and published by . This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reducing Development Costs of Large Vocabulary Speech Recognition Systems written by Thiago Fraga Da Silva and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the outstanding challenges in large vocabulary automatic speech recognition (ASR) is the reduction of development costs required to build a new recognition system or adapt an existing one to a new task, language or dialect. The state-of-the-art ASR systems are based on the principles of the statistical learning paradigm, using information provided by two stochastic models, an acoustic (AM) and a language (LM) model. The standard methods used to estimate the parameters of such models are founded on two main assumptions : the training data sets are large enough, and the training data match well the target task. It is well-known that a great part of system development costs is due to the construction of corpora that fulfill these requirements. In particular, manually transcribing the audio data is the most expensive and time-consuming endeavor. For some applications, such as the recognition of low resourced languages or dialects, finding and collecting data is also a hard (and expensive) task. As a means to lower the cost required for ASR system development, this thesis proposes and studies methods that aim to alleviate the need for manually transcribing audio data for a given target task. Two axes of research are explored. First, unsupervised training methods are explored in order to build three of the main components of ASR systems : the acoustic model, the multi-layer perceptron (MLP) used to extract acoustic features and the language model. The unsupervised training methods aim to estimate the model parameters using a large amount of automatically (and inaccurately) transcribed audio data, obtained thanks to an existing recognition system. A novel method for unsupervised AM training that copes well with the automatic audio transcripts is proposed : the use of multiple recognition hypotheses (rather than the best one) leads to consistent gains in performance over the standard approach. Unsupervised MLP training is proposed as an alternative to build efficient acoustic models in a fully unsupervised way. Compared to cross-lingual MLPs trained in a supervised manner, the unsupervised MLP leads to competitive performance levels even if trained on only about half of the data amount. Unsupervised LM training approaches are proposed to estimate standard back-off n-gram and neural network language models. It is shown that unsupervised LM training leads to additive gains in performance on top of unsupervised AM training. Second, this thesis proposes the use of model interpolation as a rapid and flexible way to build task specific acoustic models. In reported experiments, models obtained via interpolation outperform the baseline pooled models and equivalent maximum a posteriori (MAP) adapted models. Interpolation proves to be especially useful for low resourced dialect ASR. When only a few (2 to 3 hours) or no acoustic data truly matching the target dialect are available for AM training, model interpolation leads to substantial performance gains compared to the standard training methods.

Download or read book Intelligent Systems written by Cornelius T. Leondes and published by CRC Press. This book was released on 2018-10-08 with total page 2400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent systems, or artificial intelligence technologies, are playing an increasing role in areas ranging from medicine to the major manufacturing industries to financial markets. The consequences of flawed artificial intelligence systems are equally wide ranging and can be seen, for example, in the programmed trading-driven stock market crash of October 19, 1987. Intelligent Systems: Technology and Applications, Six Volume Set connects theory with proven practical applications to provide broad, multidisciplinary coverage in a single resource. In these volumes, international experts present case-study examples of successful practical techniques and solutions for diverse applications ranging from robotic systems to speech and signal processing, database management, and manufacturing.