DHMM for Speech Recognition

The article presents a DHMM (Discrete Hidden Markov Model) program specifically developed for speech recognition tasks. This implementation demonstrates practical applications of DHMMs through core algorithmic components including state transition probabilities, observation emission matrices, and the forward-backward algorithm for parameter estimation. The program employs Baum-Welch re-estimation for model training and Viterbi algorithm for optimal path decoding, which significantly enhances speech recognition performance. By utilizing this DHMM program, developers can achieve improved accuracy and efficiency in speech recognition systems through proper configuration of hidden states and observation symbols. The implementation serves as a valuable reference for expanding speech recognition applications to various domains while maintaining robust performance. Furthermore, the modular code structure allows for easy adaptation to other pattern recognition fields, making it a versatile tool for researchers and engineers working with sequential data analysis.