Fundamentals of speech recognition
Fundamentals of speech recognition
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Speech recognition in noisy environments
Speech recognition in noisy environments
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
EURASIP Journal on Applied Signal Processing
Intonation modeling for Indian languages
Computer Speech and Language
Enhancement of noisy speech by temporal and spectral processing
Speech Communication
Noise adaptive training for robust automatic speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Recognition of speech in additive and convolutional noise based on RASTA spectral processing
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Application of prosody models for developing speech systems in Indian languages
International Journal of Speech Technology
Spotting multilingual consonant-vowel units of speech using neural network models
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Noisy Constrained Maximum-Likelihood Linear Regression for Noise-Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Vowel Onset Point Detection Using Source, Spectral Peaks, and Modulation Spectrum Energies
IEEE Transactions on Audio, Speech, and Language Processing
Quantile based histogram equalization for noise robust large vocabulary speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Vowel onset point detection for noisy speech using spectral energy at formant frequencies
International Journal of Speech Technology
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This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.