원문정보
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
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