Large Vocabulary in Continuous Speech Recognition Using HMM and Normal Fit

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-42 Number-2
Year of Publication : 2016
Authors : Hemakumar G, Punithavalli M, Thippeswamy K
  10.14445/22312803/IJCTT-V42P117

MLA

Hemakumar G, Punithavalli M, Thippeswamy K  "Large Vocabulary in Continuous Speech Recognition Using HMM and Normal Fit". International Journal of Computer Trends and Technology (IJCTT) V42(2):102-107, December 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
this paper addresses the problem of large vocabulary speaker independent continuous speech recognition using the phonemes, Hidden Markov Model (HMM) and Normal fit method. Here we first detect for the voiced part in speech signal through computing dynamic threshold in each frame. Real Cepstrum coefficients are extracted as features from the voiced frames. The Baum–Welch algorithm is applied for training those features. Then normal fit technique is applied, the outputted values are labelled using correspondent phoneme or syllable. The model is tested for 5 languages namely English, Kannada, Hindi, Tamil and Telugu. The automatic segmentation of speech signals average accuracy rate is 95.42% and miss rate of about 4.58%. In the large vocabulary, average Word Recognition Rate (WRR) is 85.16% and average Word Error Rate (WER) is 14.84%. All computations are done using mat lab.

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Keywords
Automatic Speech Recognition (ASR), Speech Enhancement, Speech Perception, HMM and Normal fit method.