Machine Learning Approaches in Speech Recognition Systems

Abstract

Machine Learning has significantly transformed the field of speech recognition systems by enabling computers to understand, process, and convert human speech into textual or executable outputs with greater accuracy and efficiency. Earlier speech recognition technologies relied mainly on rule-based and statistical approaches, which often struggled with variations in accent, pronunciation, background noise, and speaking speed. The emergence of Machine Learning techniques, particularly Deep Learning, has improved the adaptability and performance of speech recognition systems across diverse linguistic and environmental conditions. Algorithms such as Hidden Markov Models (HMM), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Recurrent Neural Networks (RNN), and Deep Neural Networks (DNN) play a vital role in enhancing speech processing and language modeling. These technologies are widely applied in virtual assistants, automated customer support, voice-controlled devices, healthcare systems, and smart communication platforms. Machine Learning-based speech recognition systems also contribute to multilingual communication and accessibility for differently-abled individuals. However, challenges such as data privacy, computational complexity, accent diversity, and noise interference still affect the efficiency of these systems. the major Machine Learning approaches used in speech recognition systems, their applications, advantages, and limitations, while also examining future developments in intelligent voice-based technologies.