Recognition of speech commands using a modified neural fuzzy network and an improved GA

Author name used in this publication: K. F. LeungAuthor name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. TamCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Neural fuzzy network and genetic algorithm approach for Cantonese speech command recognition

Author name used in this publication: K. F. LeungAuthor name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. TamCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition

This paper presents the recognition of speech commands using a modified neural fuzzy network (NFN). By introducing associative memory (the tuner NFN) into the classification process (the classifier NFN), the network parameters could be made adaptive to changing input data. Then, the search space of the classification network could be enlarged by a single network. To train the parameters of the modified NFN, an improved genetic algorithm is proposed. As an application example, the proposed speech recognition approach is implemented in an eBook experimentally to illustrate the design and its merits. © Springer-Verlag London Limited 2007

A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions

Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to inter-face with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions.The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories

A Software Testbed for Assessing Human-Robot Verbal Interaction

Verbal interaction provides a natural and social-style interaction mode by which robots can communicate with general public who is likely unknowledgeable in robotics. This interaction mechanism is also very important for a broad range of users such as hands/eyes-busy users, motor-impaired users, users with vision impairment and users working in hostile environments. Verbal interaction is very popular in robotics especially in personal assistive robots, which are used to help elderly people and in entertainment robots. Several research endeavors have been assigned to endow the robots with verbal interaction as a high-level faculty. However, the language usages of many of them were simple and may not be considered as full speech dialogue systems providing natural language understanding. In this thesis, we investigate a testbed platform that can be deployed to enable human-robot verbal interaction. The proposed approach encompasses a design pattern-based user interface and a user-independent automatic speech recognizer with a modified grammar module in the context of human-robot interaction. The user interface is used to simulate robots response toward multiple users’ voice commands. The performance of the proposed testbed has been evaluated quantitatively using a set of evaluation metrics such as word correct rate, recognition time and success and false action rates. The conducted experiments show the promising features of the system. The results obtained could be refined even further by training the system for more voice commands and the whole system could be ported to real robotic platforms such as Peoplebot to endow it with natural language understanding

Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum / Soft Computing Approach

Abstract. The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular, case, the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.