Download or read book Isolated Word Recognition Using Hidden Markov Model and Neural Network written by Muhammad Hassan Saif Siddiqi and published by . This book was released on 1992 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hidden Markov Models for Isolated Word Recognition written by Fahad Nasser Alghannam and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis documents the work done to put hidden Markov models (HMMs), in a form which can be used as a pattern comparison and c1assification tool in isolated word recognition systems. The thesis starts with a general introduction for speech recognition including historical review, fields of current research and the history of implementing hidden Markov models in speech recognition. The mathematical investigation of hidden Markov models has been given, including the solutions to the recognition, training and optimal state sequence problems. Attention has been drawn to the left-to-right HMM as the most suitable model for the purpose of isolated word recognition. The considerations required for using this model in isolated word recognition have been discussed Most of the presented algorithms have been implemented in the "C" language.
Download or read book The Application of Hidden Markov Models in Speech Recognition written by Mark Gales and published by Now Publishers Inc. This book was released on 2008 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance.
Download or read book Isolated Word Recognition Using MFCC LPC VQ and Hidden Markov Model written by Mahesh Patil and published by . This book was released on 2016-09-20 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Offline Handwritten Word Recognition Using a Hybrid Neural Network and Hidden Markov Model written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Study of the Hidden Markov Model for Isolated Word Recognition written by Visvanathan Neelakantan and published by . This book was released on 1993 with total page 520 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 Hidden Markov Models for Speech Recognition written by X. D. Huang and published by . This book was released on 1990-01-01 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Nets Wirn Vietri 98 written by Maria Marinaro and published by . This book was released on 1998-12-01 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Word Recognition Using Hidden Markov Models and Neural Associative Memories written by Zöhre Kara Kayikci and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Application of Hidden Markov Models to Isolated Word Recognition written by Bruce L. Johnson and published by . This book was released on 1990 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Context Sensitive Optical Character Recognition Using Neural Networks and Hidden Markov Models written by Steven C. Elliott and published by . This book was released on 1992 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis investigates a method for using contextual information in text recognition. This is based on the premise that, while reading, humans recognize words with missing or garbled characters by examining the surrounding characters and then selecting the appropriate character. The correct character is chosen based on an inherent knowledge of the language and spelling techniques. We can then model this statistically. The approach taken by this Thesis is to combine feature extraction techniques, Neural Networks and Hidden Markov Modeling. This method of character recognition involves a three step process: pixel image preprocessing, neural network classification and context interpretation. Pixel image preprocessing applies a feature extraction algorithm to original bit mapped images, which produces a feature vector for the original images which are input into a neural network. The neural network performs the initial classification of the characters by producing ten weights, one for each character. The magnitude of the weight is translated into the confidence the network has in each of the choices. The greater the magnitude and separation, the more confident the neural network is of a given choice. The output of the neural network is the input for a context interpreter. The context interpreter uses Hidden Markov Modeling (HMM) techniques to determine the most probable classification for all characters based on the characters that precede that character and character pair statistics. The HMMs are built using an a priori knowledge of the language: a statistical description of the probabilities of digrams. Experimentation and verification of this method combines the development and use of a preprocessor program, a Cascade Correlation Neural Network and a HMM context interpreter program. Results from these experiments show the neural network successfully classified 88.2 percent of the characters. Expanding this to the word level, 63 percent of the words were correctly identified. Adding the Hidden Markov Modeling improved the word recognition to 82.9 percent."--Abstract.
Download or read book Fundamentals in Handwriting Recognition written by Sebastiano Impedovo and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 499 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many years researchers in the field of Handwriting Recognition were considered to be working in an area of minor importance in Pattern Recog nition. They had only the possibility to present the results of their research at general conferences such as the ICPR or publish their papers in journals such as some of the IEEE series or PR, together with many other papers generally oriented to the more promising areas of Pattern Recognition. The series of International Workshops on Frontiers in Handwriting Recog nition and International Conferences on Document Analysis and Recognition together with some special issues of several journals are now fulfilling the expectations of many researchers who have been attracted to this area and are involving many academic institutions and industrial companies. But in order to facilitate the introduction of young researchers into the field and give them both theoretically and practically powerful tools, it is now time that some high level teaching schools in handwriting recognition be held, also in order to unite the foundations of the field. Therefore it was my pleasure to organize the NATO Advanced Study Institute on Fundamentals in Handwriting Recognition that had its origin in many exchanges among the most important specialists in the field, during the International Workshops on Frontiers in Handwriting Recognition.
Download or read book Introduction to Hidden Markov Models and Their Applications to Classification Problems written by Michail Zambartas and published by . This book was released on 1999 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an introduction to Hidden Markov models (HMM) and their applications to classification problems. HMMs have been used extensively to model the temporal structure and variability of speech and other signals in the last decade. We selected to write our own HMM implementation in MATLAB. We tested our software on a limited isolated 4-word recognition. We also applied our implementation to the recognition of mine-like objects buried in shallow sand, using seismo-acoustic data obtained from an on-going project at the Naval Postgraduate School. Initial results indicate that the HMM-based classifier can recognize the type of mine-like object, independent of the object weight with a 97% accuracy. Results also indicate that it can recognize the object type at different distances with a 100% accuracy. However, the experiments were conducted with very few data, and further work needs to be done to confirm these initial findings by using a larger data set. Finally, we benchmarked our results against those obtained using a back-propagation neural network implementation, which were found to be similar, but slower than the HMM- based implementation.
Download or read book Study of the Hidden Markov Model in Speech Recognition written by Joseph Y. Fang and published by . This book was released on 1988 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Vector Quantization and Hidden Markov Models for Isolated Word Recognition written by Fransiska Intancahyani Harsano and published by . This book was released on 1998 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Induction to Hidden Markov Models and Their Applications to Classification Problems written by Michail Zambartas and published by . This book was released on 1999-09-01 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents an introduction to Hidden Markov models (HMM) and their applications to classification problems. HMMs have been used extensively to model the temporal structure and variability of speech and other signals in the last decade. We selected to write our own HMM implementation in MATLAB. We tested our software on a limited isolated 4-word recognition. We also applied our implementation to the recognition of mine-like objects buried in shallow sand, using seismo-acoustic data obtained from an on-going project at the Naval Postgraduate School. Initial results indicate that the HMM-based classifier can recognize the type of mine-like object, independent of the object weight with a 97% accuracy. Results also indicate that it can recognize the object type at different distances with a 100% accuracy. However, the experiments were conducted with very few data, and further work needs to be done to confirm these initial findings by using a larger data set. Finally, we benchmarked our results against those obtained using a back-propagation neural network implementation, which were found to be similar, but slower than the HMM- based implementation.
