A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Fundamentals of speech recognition
Fundamentals of speech recognition
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Bangla Speech Recognition System Using LPC and ANN
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Continuous Malayalam speech recognition using Hidden Markov Models
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
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Speech recognition is a fascinating application of digital signal processing offering unparalleled opportunities. In this paper, a comparative study of different feature extraction techniques like Linear Predictive Coding (LPC), Discrete Wavelet Transforms (DWT) and Wavelet packet Decomposition (WPD) are employed for recognizing speaker independent spoken isolated words. Voice signals are sampled directly from the microphone and then they are processed using these three techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). This work includes three speech recognition methods. First one is a hybrid approach with LPC and ANN, second method uses a combination of DWT and ANN and the third one utilizes a combination of WPD and ANN. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. All the three methods produce good recognition accuracy. LPC based method produced an accuracy of 81.20%, DWT gave an accuracy of 90% and WPD produced a recognition accuracy of 87.50%. Thus wavelet based methods are found to be more suitable for recognizing speech because of their multi-resolution characteristics and efficient time frequency localizations. Moreover, wavelet methods have a better capability to model the unvoiced sound details.