Machine Learning for Automatic Speech Recognition

Summary

It is very important to be able to use speech signals correctly so that you can share your feelings, thoughts, and information about the real world, as well as for your day‐to‐day work. Speech is the most natural way for people to talk to each other verbally. With the help of speech recognition technology, computers can now understand human languages and respond to spoken commands. At the heart of the field of voice recognition is the development of methods and platforms for analyzing spoken and natural language. It is put into the computer these days so that the right processing and rearranging can happen. Over the past 10 years, automatic speech recognizers have gotten much better at what they do. Most studies have looked at the many technologies that are now available to help people who have trouble speaking. Research shows that voice recognition applications for people who have trouble speaking are well within the capabilities of modern technology. However, there is not a lot of work that looks at the human side of things, which is the main thing holding back progress in this field. Concerns have been raised about a number of things that have to do with people. Speech recognition has the most potential to change the lives of people who have trouble talking. This chapter presents a machine learning‐based framework for automatic speech recognition. Speech acquisition, speech preprocessing, feature extraction, and classification are the main components of the proposed framework.