Recognition Using DNN with Bacterial Foraging Optimization Using MFCC Coefficients

Abstract

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.