Abstract

As machine learning models have advanced, speech recognition systems have become increasingly common. Virtual assistants, transcription software, and automated customer support are now powered by these systems. Performance, accuracy, and flexibility have increased with the use of machine learning techniques including Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), and Hidden Markov Models (HMM). The main mathematical ideas underlying these models are examined in this work along with an example Java-based implementation and an analysis of current issues such data limits, speaker variability, and noise reduction. Future options for enhancing voice recognition with cutting-edge methods like transformer models and unsupervised learning are discussed in the paper’s conclusion.

Keywords: Machine learning, Speech recognition, RNN, DNN, HMM, Java-based implementation