Speech Communication - Special issue on speech processing in adverse conditions
Swarm intelligence
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
A genetic classification error method for speech recognition
Signal Processing
Data Driven Design of Filter Bank for Speech Recognition
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Optimization of HMM by a Genetic Algorithm
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Modeling durations of syllables using neural networks
Computer Speech and Language
Springer Handbook of Speech Processing
Springer Handbook of Speech Processing
Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)
International Journal of Speech Technology
International Journal of Speech Technology
New insights into the noise reduction Wiener filter
IEEE Transactions on Audio, Speech, and Language Processing
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
A novel approach to HMM-based speech recognition systems using particle swarm optimization
Mathematical and Computer Modelling: An International Journal
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Automatic speech recognition (ASR) systems follow a well established approach of pattern recognition, that is signal processing based feature extraction at front-end and likelihood evaluation of feature vectors at back-end. Mel-frequency cepstral coefficients (MFCCs) are the features widely used in state-of-the-art ASR systems, which are derived by logarithmic spectral energies of the speech signal using Mel-scale filterbank. In filterbank analysis of MFCC there is no consensus for the spacing and number of filters used in various noise conditions and applications. In this paper, we propose a novel approach to use particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the parameters of MFCC filterbank such as the central and side frequencies. The experimental results show that the new front-end outperforms the conventional MFCC technique. All the investigations are conducted using two separate classifiers, HMM and MLP, for Hindi vowels recognition in typical field condition as well as in noisy environment.