Open AccessDigit Speech Recognition u sing Hidden Markov Model Toolkit
Author(s) -
Chayan Paul,
Pronami Bora
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2540.039520
Subject(s) - speech recognition , hidden markov model , computer science , digit recognition , mel frequency cepstrum , utterance , numerical digit , task (project management) , transcription (linguistics) , phonetic transcription , artificial intelligence , speaker recognition , speech processing , pattern recognition (psychology) , feature extraction , artificial neural network , mathematics , engineering , linguistics , philosophy , arithmetic , systems engineering
Digit speech recognition refers to the task of identifying the English digit spoken in a particular utterance by an unknown speaker. The conventional methods used for the recognition of digits in speech are based on robust pattern recognition techniques which deal with the statistical parameters of speech. HMM, GMM and dynamic programming techniques are some of the methods. This paper presents recognition of digits using HTK Toolkit which is based on Hidden Markov Model using MFCC. The digit speech database of this work was collected in real time from both male and female speakers and the transcription of the total collected data was done using Wavesurfer.