Research Applications of Hidden Markov Models in Speech Recognition

Reinforcement Learning, a vital branch of Machine Learning, has gained significant attention due to its interactive and goal-oriented learning approach. Its primary objective is to discover the optimal strategy within a continuous time series, thereby enhancing performance and efficiency. In the realm of speech recognition, accurate recognition and prediction of subsequent content have always been pivotal in Artificial Intelligence research. This article centres on the application of advanced Hidden Markov Models in Reinforcement Learning, with particular emphasis on their efficacy in complex speech recognition tasks. By refining the model, researchers delve into the utility of intelligent recognition algorithms in speech recognition contexts. Through a series of meticulous experiments, it was ascertained that the enhanced algorithm demonstrates superior performance in speech recognition compared to its predecessors. This means that by introducing an improved hidden Markov model, the accuracy and predictive ability of speech recognition systems can be improved. The experimental results show that the improved algorithm performs better in speech recognition, providing new ideas for improving system performance and prediction ability.