Key points are not available for this paper at this time.
Artificial intelligence (AI) is the term used to describe the intelligence exhibited by machines, allowing them to perform tasks efficiently. AI has brought about a revolution in the field of speech recognition, enabling machines to comprehend and interpret human speech with an impressive degree of accuracy. Speech recognition is the process of a computer understanding and responding to voice input for various tasks. AI is built upon two fundamental principles. Firstly, it involves studying human thought processes, and secondly, it entails representing and simulating these processes through machines, such as computers and robots. One of the primary advantages of a speech recognition system is its ability to allow users to multitask. Users can focus on observations and manual tasks while controlling machinery through voice commands. Speech signal classification is a challenging yet pivotal task in the field of signal processing and machine learning. This study investigates the potential of neural networks as formidable models for this purpose, emphasizing the influence of pre- processing techniques on their performance. Our findings highlight the proficiency of even simplified neural network models in recognizing a limited set of words, showcasing their versatility in speech-related tasks
Batra et al. (Fri,) studied this question.