Using multiple vector quantization and semicontinuous hidden Markov models for speech recognition

Use of multiple vector quantisation for semicontinuous-HMM speech recognition - Vision, Image and Signal Processing, IEE Proceedings-

Although the continuous hidden Markov model (CHMM) technique seems to be the most flexible and complete tool for speech modelling, it is not always used for the implementation of speech recognition systems because of several problems related to training and computational complexity. Thus, other simpler types of HMMs, such as discrete (DHMM) or semicontinuous (SCHMM) models, are commonly utilise...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Hidden Markov Models in Wavelet Analysis

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...

Isolated Word Recognition on an Embedded System

In this paper, an embedded isolated word recognition system, which is designed for command and control applications, is presented. The hardware is designed by using a 32 bit general purpose ARM7TDMI cored microcontroller and required peripherals. Vector Quantization (VQ) and Discrete Hidden Markov Model (HMM) based speech recognition algorithms are implemented on the embedded system. The system...