Digit Speech Recognition using Hidden Markov Model Toolkit
Chayan Paul1, Pronami Bora2

1Chayan Paul*, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India.
2Pronami Bora, Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 23, 2020. | Manuscript published on March 10, 2020. | PP: 917-921 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2540039520/2020©BEIESP | DOI: 10.35940/ijitee.E2540.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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. 
Keywords: HMM, MFCC,
Scope of the Article: Probabilistic Models and Methods