Earticle

초록

영어

Connected digits recognition is important in many applications such as voice-dialing telephone, automated banking system, automatic data entry, PIN entry, etc. In this paper robust features such as Revised perceptual linear prediction (RPLP), Bark frequency cepstral coefficients (BFCC) and Mel frequency perceptual linear prediction (MF-PLP) are used for speaker-independent connected Hindi digits recognition in clean and noisy environments. The recognition performance of these features is compared with recognition performance of Mel frequency cepstral coefficient (MFCC), ΔMFCC and Perceptual linear prediction (PLP) features. MF-PLP features have shown best recognition efficiency for clean as well as for noisy database. MFCC features are calculated by using feature extraction tool of Hidden Markov model Toolkit (HTK). All other features except MFCC are calculated using Matlab and saved in HTK format. Training and testing for speech recognition is done using HTK.

목차

Abstract
 1. Introduction
 2. Database Preparation
 3. Robust Feature Selection
  3.1. Mel Frequency Cepstral Coefficients (MFCC) and ΔMFCC
  3.2. Perceptual Features (PLP and MF-PLP)
  3.3. Hybrid Features (BFCC & RPLP)
 4. Recognizer Model
 5. Hidden Markov Model Toolkit
 6. Experimental Results
  6.1 Effect of Using Different Features for Clean Database
  6.2 Effect of using different features for noisy database
 7. Conclusion
 References

키워드

저자

• A. N. Mishra [ Department of ECE, BIT, Mesra, Ranchi, India ]
• Mahesh Chandra [ Department of ECE, BIT, Mesra, Ranchi, India ]
• Astik Biswas [ Department of ECE, BIT, Mesra, Ranchi, India ]
• S. N. Sharan [ Director GNIT, Greater Noida, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605