Total views : 230
A HMM Integrated SVM Model for Hindi Speech Recognition
Speech is one of the most useful and effective communication medium used as the biometric feature of human being. Speech Recognition is considered as important part of various applications for biometric recognition and translation. Speech can be acquired inexpensively using some mic or phone device. But because of different noise factors that can be included during acquisition increases the complexity in recognition process. Because of this, there is the requirement of more effective and reliable approach for speech recognition. In this present work, a statistical analysis based predictive model is defined to improve the speech recognition. The presented work is defined in the form of a layered model. In first layer of this model, the speech signal improvement is done. To achieve this, the Discrete Wavelet Transform (DWT) and spectral subtraction based approach is defined for noise reduction. This layer improves the speech features. In second layer, the Hidden Markov Model (HMM) improved statistical model is defined to generate the speech features. This layer uses the segmented mean value, standard deviation, variation analysis as the statistical features. In final stage, Distance based mapping is applied on all these features collectively to perform the recognition. The work is implemented for Hindi word recognition. The work is implemented in Matlab environment.
Discrete Wavelet Transform (DWT), Hidden Markov Model (HMM), Speech Recognition, Spectral Subtraction, Support Vector Machine (SVM).
- Peinado AM. Use of multiple vector quantisation for semicontinuous-HMM speech recognition. IEEE Proc-Vis.Image Signal Process, 1994.
- Tatsuhiko Kinjo. On Hmm Speech Recognition Based on Complex Speech Analysis. IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference, 6-10 Nov. 2006.
- Cong-Thanh Do. On the Recognition of Cochlear Implant-Like Spectrally Reduced Speech With MFCC and HMM-Based ASR. IEEE Transactions On Audio, Speech, And Language Processing. 2010 July; 18(5):1065-68.
- Panikos Heracleous. Analysis and Recognition of NAM Speech Using HMM Distances and Visual Information.IEEE Transactions on Audio, Speech, and Language Processing. 2010 Aug; 18(6): 1528-38.
- Revathi A. Speaker Independent Continuous Speech and Isolated Digit Recognition using VQ and HMM.Communications and Signal Processing (ICCSP), 2011 International Conference. 10-12 Feb. 2011.
- Jinyu Li. Improving Wideband Speech Recognition Using Mixed-Bandwidth Training Data in CD-DNN-HMM. SLT 2012, IEEE Workshop on Spoken Language Technology, Inproceedings, January 1, 2012.
- Ashok Shigli. A Spectral Feature Process for Speech Recognition Using HMM With MFCC Approach. 2012 National Conference on Computing and Communication Systems (NCCCS). 21-22 Nov. 2012.
- Mohit Dua. Punjabi Automatic Speech Recognition using HTK. IJCSI International Journal of Computer Science Issues. 2012 Jul; 9(4):359-64.
- Preeti Saini. Hindi Automatic Speech Recognition Using HTK. International Journal of Engineering Trends and Technology (IJETT). 2013 Jun; 4(6):2223-29.
- Sunija AP, Rajisha TM, Riyas KS. Comparative Study of Different Classifiers for Malayalam Dialect Recognition System. Procedia Technol. 2016; 24:1080-88.
- Rajisha TM, Sunija AP, Riyas KS. Performance Analysis of Malayalam Language Speech Emotion Recognition System Using ANN/SVM. Procedia Technol. 2016; 24:1097-1104.
- Hoesen D, Satriawan CH, Lestari DP, Khodra ML. Towards Robust Indonesian Speech Recognition with SpontaneousSpeech Adapted Acoustic Models. Procedia Comput. Sci.2016 May; 81:167-73.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.