Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identified by modified MFCC algorithm. The classification of features is done by back propagation algorithm.

     

     


  • Keywords


    Artificial Neural Network; Back Propagation Network; Mel Frequency Cepstral Coefficients; Speech Accent.

  • References


      [1] Robert Rehr, Timo Gerkamnn (2018), “On the Importance of Super Gaussian Speech Priors for Machine Learning Based Speech Enhancement”, IEEE.

      [2] Narendra D. Londhe, Ghanahshyam B. Kshirasagar (2017), “Speaker Independent Isolated Words Recognition System for Chhattisgarhi Dialect”, ICIIECS.

      [3] Anand H. Unnibhavi, D.S. Jangamshetti (2017), LPC Based Speech Recognition for Kannada Vowels, ICEECCOT.

      [4] Sanjay Bhardwaj, Sunil Pathania, Rajesh Akela (2015), Speech Recognition using Hidden Markov Model and Viterbi Algorithm, IJARECE.

      [5] Kamil Kaminski, Ewelina Majda, Andrzej P. Dobrowolski (2013), Automatic Speaker Recognition using a unique personal feature vector and Gaussian Mixture Models, IEEE.

      [6] U.G. Patil, S.D. Shirbahadurkar, A.N. Paithane (2016). Automatic Speech Recognition of Isolated Words in Hindi Language using MFCC, CAST.


 

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Article ID: 14202
 
DOI: 10.14419/ijet.v7i2.33.14202




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