Improved Hybrid Model of HMM/GMM for Speech Recognition

In this paper, we propose a speech recognition engine using hybrid model of Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Both the models have been trained independently and the respective likelihood values have been considered jointly and input to a decision logic which provides net likelihood as the output. This hybrid model has been compared with the HMM model. Training and testing has been done by using a database of 20 Hindi words spoken by 80 different speakers. Recognition rates achieved by normal HMM are 83.5% and it gets increased to 85% by using the hybrid approach of HMM and GMM.

A Cognitive Science Reasoning in Recognition of Emotions in Audio-Visual Speech

In this report we summarize the state-of-the-art of speech emotion recognition from the signal processing point of view. On the bases of multi-corporal experiments with machine-learning classifiers, the observation is made that existing approaches for supervised machine learning lead to database dependent classifiers which can not be applied for multi-language speech emotion recognition without additional training because they discriminate the emotion classes following the used training language. As there are experimental results showing that Humans can perform language independent categorisation, we made a parallel between machine recognition and the cognitive process and tried to discover the sources of these divergent results. The analysis suggests that the main difference is that the speech perception allows extraction of language independent features although language dependent features are incorporated in all levels of the speech signal and play as a strong discriminative function in human perception. Based on several results in related domains, we have suggested that in addition, the cognitive process of emotion-recognition is based on categorisation, assisted by some hierarchical structure of the emotional categories, existing in the cognitive space of all humans. We propose a strategy for developing language independent machine emotion recognition, related to the identification of language independent speech features and the use of additional information from visual (expression) features.

Optimization of Gabor Wavelets for Face Recognition

The article describes researches of a method of person recognition by face image based on Gabor wavelets. Scales of Gabor functions are determined at which the maximal percent of recognition for search of a person in a database and minimal percent of mistakes due to false alarm errors when solving an access control task is achieved. The carried out researches have shown a possibility of improvement of recognition system work parameters in the specified two modes when the volume of used data is reduced.

Intelligent System for Computer Aided Assembly Process Planning

This paper presents the concepts of the intelligent system for aiding of the module assembly technology. The first part of this paper presents a project of intelligent support system for computer aided assembly process planning. The second part includes a coincidence description of the chosen aspects of implementation of this intelligent system using technologies of artificial intelligence (artificial neural networks, fuzzy logic, expert systems and genetic algorithms).