This paper presents clustering techniques that partition temporal data into homogeneous groups, and constructs state based proles for each group in the hidden Markov model (HMM) framework. We propose a Bayesian HMM clustering methodology that improves upon existing HMM clustering by incorporating HMM model size selection into clustering control structure to derive better cluster models and part...

Good HMM-based speech recognition performance requires at most minimal inaccuracies to be introduced by HMM conditional independence assumptions. In this work, HMM conditional independence assumptions are relaxed in a principled way. For each hidden state value, additional dependencies are added between observation elements to increase both accuracy and discriminability. These additional depend...

Vision based hand gesture recognition systems track the hands and extract their spatial trajectory and shape information, which are then classified with machine learning methods. In this work, we propose a dynamic time warping (DTW) based pre-clustering technique to significantly improve hand gesture recognition accuracy of various graphical models used in the human computer interaction (HCI) l...

In this paper, we propose a system for recognition handwritten characters Tifinagh, with the use of neural networks (the multi layer perceptron MLP), the hidden Markov model (HMM), the hybrid Model MLP/HMM and a feature extraction method based on mathematical morphology, this method is tested on the database of handwritten isolated characters Tifinagh size consistent (1800 images in learning an...

This paper examines audio-visual speaker veri cation using a novel adaptation of fused hidden Markov models, in comparison to output fusion of individual classi ers in the audio and video modalities. A comparison of both hidden Markov model (HMM) and Gaussian mixture model (GMM) classi ers in both modalities under output fusion shows that the choice of audio classi er is more important than vid...

There are many artificial intelligence approaches used in the development of Automatic Speech Recognition (ASR), hybrid approach is one of them. The common hybrid method in speech recognition is the combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM). The hybrid ANN/HMM is able to classify the phoneme model and to combine the strength of HMM in sequential modeling struc...

This paper focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the precence of additive car noise. The technique applied is based on a combination of the Hidden Markov Model (HMM) decomposition method [ 11, for speech recognition...

The paper deals with a mathematical model using a structure designed for an application of pattern recognition in ECG signals. The overall process of recognition is consist of generation of a code book, vector quantization, Markov model learning, and recognition. A Hidden Markov Model (HMM) structure with vector–valued observation sequences can be used for the characterization of cardiac arryth...

We propose an algorithm for the construction of a Hidden Markov Model (HMM) of assigned complexity (number of states of the underlying Markov chain) which best approximates, in Kullback-Leibler divergence rate, a given stationary process. We establish, under mild conditions, the existence of the divergence rate between a stationary process and an HMM, and approximate it with a properly defined ...