Isolated Word Recognition using HMM for Maithili dialect

Abstract

In this work, an Automatic Speech Recognition (ASR) system of isolated words for Maithili dialect is presented. Isolated Word Recognition (IWR) systems recognize a spoken word by a person through a microphone or some other devices. In this work, we have centered to perceive Maithili dialect words utilized for telephone security and internet searchers, or programmed dialing and managing banking security frameworks. The speech signals of Maithili words are changed over into an arrangement of highlight vectors and the elements utilized are Mel-Frequency Cepstral Coefficients (MFCC). The IWR is demonstrated with these element vectors utilizing Hidden Markov Model (HMM). The HMM-based dialect models and acoustic models with a vocabulary of words have been utilized for computing misclassification rate for Maithili words covering every one of the vowels of the Maithili phoneme utilizing five folded cross-validation process.