Speech Recognition Classification with ANN Implementation Using Machine Learning Algorithm

The acoustic characteristics of SR production are treated as a separate issue with reliable models for predicting and classification decisions. The importance of the representation learning paradigm improves behaviours and makes assignments less reliant on human experience (i.e., classification, prediction, etc). The SR solution proposes to distinguish text from speech, which would aid people with hearing impairments in completing their activities. This research work aims to provide a method for converting speech into a machine-readable script that can be used in libraries and other workplaces for efficient management and adequate performance. The usage of supervised learning and ANN understand speech for massive datasets are suggested as a technical interpretation of SR. In this work, SR uses SVM to observe the protocol and have an acceptable performance that is versatile and easy to use and allows for articulate and safe talk. In addition, the SR accuracy with CNN is provided in this research work, which calculates the optimum performance as a consequence.