Embedding speech into virtual realities

In this work a speaker-independent speech recognition system is presented, which is suitable for implementation in Virtual Reality applications. The use of an artificial neural network in connection with a special compression of the acoustic input leads to a system, which is robust, fast, easy to use and needs no additional hardware, beside a common VR-equipment

Applying Levenberg-Marquardt algorithm with block-diagonal Hessian approximation to recurrent neural network training.

by Chi-cheong Szeto.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 162-165).Abstracts in English and Chinese.Abstract --- p.iAcknowledgment --- p.iiTable of Contents --- p.iiiChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Time series prediction --- p.1Chapter 1.2 --- Forecasting models --- p.1Chapter 1.2.1 --- Networks using time delays --- p.2Chapter 1.2.1.1 --- Model description --- p.2Chapter 1.2.1.2 --- Limitation --- p.3Chapter 1.2.2 --- Networks using context units --- p.3Chapter 1.2.2.1 --- Model description --- p.3Chapter 1.2.2.2 --- Limitation --- p.6Chapter 1.2.3 --- Layered fully recurrent networks --- p.6Chapter 1.2.3.1 --- Model description --- p.6Chapter 1.2.3.2 --- Our selection and motivation --- p.8Chapter 1.2.4 --- Other models --- p.8Chapter 1.3 --- Learning methods --- p.8Chapter 1.3.1 --- First order and second order methods --- p.9Chapter 1.3.2 --- Nonlinear least squares methods --- p.11Chapter 1.3.2.1 --- Levenberg-Marquardt method ´ؤ our selection and motivation --- p.13Chapter 1.3.2.2 --- Levenberg-Marquardt method - algorithm --- p.13Chapter 1.3.3 --- "Batch mode, semi-sequential mode and sequential mode of updating" --- p.15Chapter 1.4 --- Jacobian matrix calculations in recurrent networks --- p.15Chapter 1.4.1 --- RTBPTT-like Jacobian matrix calculation --- p.15Chapter 1.4.2 --- RTRL-like Jacobian matrix calculation --- p.17Chapter 1.4.3 --- Comparison between RTBPTT-like and RTRL-like calculations --- p.18Chapter 1.5 --- Computation complexity reduction techniques in recurrent networks --- p.19Chapter 1.5.1 --- Architectural approach --- p.19Chapter 1.5.1.1 --- Recurrent connection reduction method --- p.20Chapter 1.5.1.2 --- Treating the feedback signals as additional inputs method --- p.20Chapter 1.5.1.3 --- Growing network method --- p.21Chapter 1.5.2 --- Algorithmic approach --- p.21Chapter 1.5.2.1 --- History cutoff method --- p.21Chapter 1.5.2.2 --- Changing the updating frequency from sequential mode to semi- sequential mode method --- p.22Chapter 1.6 --- Motivation for using block-diagonal Hessian matrix --- p.22Chapter 1.7 --- Objective --- p.23Chapter 1.8 --- Organization of the thesis --- p.24Chapter Chapter 2 --- Learning with the block-diagonal Hessian matrix --- p.25Chapter 2.1 --- Introduction --- p.25Chapter 2.2 --- General form and factors of block-diagonal Hessian matrices --- p.25Chapter 2.2.1 --- General form of block-diagonal Hessian matrices --- p.25Chapter 2.2.2 --- Factors of block-diagonal Hessian matrices --- p.27Chapter 2.3 --- Four particular block-diagonal Hessian matrices --- p.28Chapter 2.3.1 --- Correlation block-diagonal Hessian matrix --- p.29Chapter 2.3.2 --- One-unit block-diagonal Hessian matrix --- p.35Chapter 2.3.3 --- Sub-network block-diagonal Hessian matrix --- p.35Chapter 2.3.4 --- Layer block-diagonal Hessian matrix --- p.36Chapter 2.4 --- Updating methods --- p.40Chapter Chapter 3 --- Data set and setup of experiments --- p.41Chapter 3.1 --- Introduction --- p.41Chapter 3.2 --- Data set --- p.41Chapter 3.2.1 --- Single sine --- p.41Chapter 3.2.2 --- Composite sine --- p.42Chapter 3.2.3 --- Sunspot --- p.43Chapter 3.3 --- Choices of recurrent neural network parameters and initialization methods --- p.44Chapter 3.3.1 --- "Choices of numbers of input, hidden and output units" --- p.45Chapter 3.3.2 --- Initial hidden states --- p.45Chapter 3.3.3 --- Weight initialization method --- p.45Chapter 3.4 --- Method of dealing with over-fitting --- p.47Chapter Chapter 4 --- Updating methods --- p.48Chapter 4.1 --- Introduction --- p.48Chapter 4.2 --- Asynchronous updating method --- p.49Chapter 4.2.1 --- Algorithm --- p.49Chapter 4.2.2 --- Method of study --- p.50Chapter 4.2.3 --- Performance --- p.51Chapter 4.2.4 --- Investigation on poor generalization --- p.52Chapter 4.2.4.1 --- Hidden states --- p.52Chapter 4.2.4.2 --- Incoming weight magnitudes of the hidden units --- p.54Chapter 4.2.4.3 --- Weight change against time --- p.56Chapter 4.3 --- Asynchronous updating with constraint method --- p.68Chapter 4.3.1 --- Algorithm --- p.68Chapter 4.3.2 --- Method of study --- p.69Chapter 4.3.3 --- Performance --- p.70Chapter 4.3.3.1 --- Generalization performance --- p.70Chapter 4.3.3.2 --- Training time performance --- p.71Chapter 4.3.4 --- Hidden states and incoming weight magnitudes of the hidden units --- p.73Chapter 4.3.4.1 --- Hidden states --- p.73Chapter 4.3.4.2 --- Incoming weight magnitudes of the hidden units --- p.73Chapter 4.4 --- Synchronous updating methods --- p.84Chapter 4.4.1 --- Single λ and multiple λ's synchronous updating methods --- p.84Chapter 4.4.1.1 --- Algorithm of single λ synchronous updating method --- p.84Chapter 4.4.1.2 --- Algorithm of multiple λ's synchronous updating method --- p.85Chapter 4.4.1.3 --- Method of study --- p.87Chapter 4.4.1.4 --- Performance --- p.87Chapter 4.4.1.5 --- Investigation on long training time: analysis of λ --- p.89Chapter 4.4.2 --- Multiple λ's with line search synchronous updating method --- p.97Chapter 4.4.2.1 --- Algorithm --- p.97Chapter 4.4.2.2 --- Performance --- p.98Chapter 4.4.2.3 --- Comparison of λ --- p.100Chapter 4.5 --- Comparison between asynchronous and synchronous updating methods --- p.101Chapter 4.5.1 --- Final training time --- p.101Chapter 4.5.2 --- Computation load per complete weight update --- p.102Chapter 4.5.3 --- Convergence speed --- p.103Chapter 4.6 --- Comparison between our proposed methods and the gradient descent method with adaptive learning rate and momentum --- p.111Chapter Chapter 5 --- Number and sizes of the blocks --- p.113Chapter 5.1 --- Introduction --- p.113Chapter 5.2 --- Performance --- p.113Chapter 5.2.1 --- Method of study --- p.113Chapter 5.2.2 --- Trend of performance --- p.115Chapter 5.2.2.1 --- Asynchronous updating method --- p.115Chapter 5.2.2.2 --- Synchronous updating method --- p.116Chapter 5.3 --- Computation load per complete weight update --- p.116Chapter 5.4 --- Convergence speed --- p.117Chapter 5.4.1 --- Trend of inverse of convergence speed --- p.117Chapter 5.4.2 --- Factors affecting the convergence speed --- p.117Chapter Chapter 6 --- Weight-grouping methods --- p.125Chapter 6.1 --- Introduction --- p.125Chapter 6.2 --- Training time and generalization performance of different weight-grouping methods --- p.125Chapter 6.2.1 --- Method of study --- p.125Chapter 6.2.2 --- Performance --- p.126Chapter 6.3 --- Degree of approximation of block-diagonal Hessian matrix with different weight- grouping methods --- p.128Chapter 6.3.1 --- Method of study --- p.128Chapter 6.3.2 --- Performance --- p.128Chapter Chapter 7 --- Discussion --- p.150Chapter 7.1 --- Advantages and disadvantages of using block-diagonal Hessian matrix --- p.150Chapter 7.1.1 --- Advantages --- p.150Chapter 7.1.2 --- Disadvantages --- p.151Chapter 7.2 --- Analysis of computation complexity --- p.151Chapter 7.2.1 --- Trend of computation complexity of each calculation --- p.154Chapter 7.2.2 --- Batch mode of updating --- p.155Chapter 7.2.3 --- Sequential mode of updating --- p.155Chapter 7.3 --- Analysis of storage complexity --- p.156Chapter 7.3.1 --- Trend of storage complexity of each set of variables --- p.157Chapter 7.3.2 --- Trend of overall storage complexity --- p.157Chapter 7.4 --- Parallel implementation --- p.158Chapter 7.5 --- Alternative implementation of weight change constraint --- p.158Chapter Chapter 8 --- Conclusions --- p.160References --- p.16

Proceedings of the 1993 Conference on Intelligent Computer-Aided Training and Virtual Environment Technology, Volume 1

These proceedings are organized in the same manner as the conference's contributed sessions, with the papers grouped by topic area. These areas are as follows: VE (virtual environment) training for Space Flight, Virtual Environment Hardware, Knowledge Aquisition for ICAT (Intelligent Computer-Aided Training) & VE, Multimedia in ICAT Systems, VE in Training & Education (1 & 2), Virtual Environment Software (1 & 2), Models in ICAT systems, ICAT Commercial Applications, ICAT Architectures & Authoring Systems, ICAT Education & Medical Applications, Assessing VE for Training, VE & Human Systems (1 & 2), ICAT Theory & Natural Language, ICAT Applications in the Military, VE Applications in Engineering, Knowledge Acquisition for ICAT, and ICAT Applications in Aerospace

Evolving systems for connectionist-based speech recognition

xv, 519 p. ; 30 cm. Includes bibliographical references. University of Otago department: Information Science. "June 18, 2003".Although studied for several years, speech recognition is still a field that is developing. Recently several important researchers have pointed out areas within the field that need to be addressed. These include robustness to various environments, large or expandable vocabularies, user-friendliness, high recognition accuracy and the ability to recognise continuous speech. The ability to adapt is an important component of a speech recognition system. People new to the system should have the benefits mentioned above. The system should also manage recognition of different speaking rates. Also, novel environments may cause a drop in the system's performance if it lacks robustness or the ability to adapt. A common target for speech recognition algorithms is to detect the presence of speech units, commonly phonemes. This approach involves grouping speech sounds, or phones, into abstract groups that reflect meaning. Recently artificial neural networks have been applied to this task. Nevertheless, uncertainty and ambiguity are inherent in the neural network recognition process. Several novel techniques are proposed to aid in the recognition process, and to help to fulfil the requirements of a successful speech recognition system. The goal of this research is to investigate theories of speech and language processing that are relevant to speech recognition and spoken language understanding. These theories have their foundations in fields such as engineering, computer science, linguistics, natural language processing, psycholinguistics and psychology. An adaptive system is implemented to test the validity and usefulness of such work to the fields of speech recognition and spoken language understanding. For example, the development of abstract structures of the human auditory system and the auditory cortex are investigated, and applied towards better engineering methods for building adaptive speech and language systems. For the implementation of an adaptive speech recognition system, parameters are introduced that can be adjusted either manually or automatically. In this manner, the system can adapt to new speakers and environments. The architecture of the system is modular and hierarchical. Different methods are applied at various levels. For example, artificial neural networks are best suited for low-level processing. A discussion of how errors and uncertainty may be resolved in an unsupervised manner concludes the work. Ideally, the system will adapt to the situation, and the future occurrences of such phenomena may be reduced or eliminated.UnpublishedAbu Hosan, R., Boucher, P., Brugnara, F., De Mori, R., Galler, M., and Snow, M. (1995). Acoustic modeling. 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Language and Speech. W. H. Freeman. Miller, G. A. and Nicely, P. E. (1955). An analysis of perceptual confusions among some english consonants. Journal of the Acoustical Society of America, 27:338-46. Mitra, S. and Pal, S. K. (1992). Rule generation and inferencing with a layer

Evolving systems for connectionist-based speech recognition

xv, 519 p. ; 30 cm. Includes bibliographical references. University of Otago department: Information Science. "June 18, 2003".Although studied for several years, speech recognition is still a field that is developing. Recently several important researchers have pointed out areas within the field that need to be addressed. These include robustness to various environments, large or expandable vocabularies, user-friendliness, high recognition accuracy and the ability to recognise continuous speech. The ability to adapt is an important component of a speech recognition system. People new to the system should have the benefits mentioned above. The system should also manage recognition of different speaking rates. Also, novel environments may cause a drop in the system's performance if it lacks robustness or the ability to adapt. A common target for speech recognition algorithms is to detect the presence of speech units, commonly phonemes. This approach involves grouping speech sounds, or phones, into abstract groups that reflect meaning. Recently artificial neural networks have been applied to this task. Nevertheless, uncertainty and ambiguity are inherent in the neural network recognition process. Several novel techniques are proposed to aid in the recognition process, and to help to fulfil the requirements of a successful speech recognition system. The goal of this research is to investigate theories of speech and language processing that are relevant to speech recognition and spoken language understanding. These theories have their foundations in fields such as engineering, computer science, linguistics, natural language processing, psycholinguistics and psychology. An adaptive system is implemented to test the validity and usefulness of such work to the fields of speech recognition and spoken language understanding. For example, the development of abstract structures of the human auditory system and the auditory cortex are investigated, and applied towards better engineering methods for building adaptive speech and language systems. For the implementation of an adaptive speech recognition system, parameters are introduced that can be adjusted either manually or automatically. In this manner, the system can adapt to new speakers and environments. The architecture of the system is modular and hierarchical. Different methods are applied at various levels. For example, artificial neural networks are best suited for low-level processing. A discussion of how errors and uncertainty may be resolved in an unsupervised manner concludes the work. Ideally, the system will adapt to the situation, and the future occurrences of such phenomena may be reduced or eliminated.UnpublishedAbu Hosan, R., Boucher, P., Brugnara, F., De Mori, R., Galler, M., and Snow, M. (1995). Acoustic modeling. 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