Speech Recognition Using Hidden Markov Model
Speech Recognition Using Hidden Markov Model
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Date
2015-04-30
Authors
Mohammad, Nisreen Ibrahim
Journal Title
Journal ISSN
Volume Title
Publisher
UOFK
Abstract
Automatic speech recognition (ASR) is achieved through
various models among which, Hidden Markov Model (HMM) gives
optimum probabilities for speech extraction. The power of the
HMM lies in dividing the signal probability function into smaller
divisions (thus overcoming the condition of stationarity). Also the
recursive nature of the model gives better convergence, therefore
more accurate results.
This thesis focuses on use of HMM for automatic speech
recognition. Towards this purpose MATLAB was used to simulate
the HMM behaviour. The features codebook was build from the
speech files of the isolated words YES, NO, CAT were recorded for
many times from the same person and recognized the 10 (YES, NO,
CAT) words spoken by the same person as hidden Markov model’s
output.
Description
Keywords
Speech,Recognition,Hidden,Markov,Model