Fundamentals of speech recognition
Fundamentals of speech recognition
Nonlinear time series analysis
Nonlinear time series analysis
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast k-Nearest Neighbor Classification Using Cluster-Based Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Guide to OCR for Indic Scripts: Document Recognition and Retrieval
Guide to OCR for Indic Scripts: Document Recognition and Retrieval
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Multi-criteria ABC analysis using artificial-intelligence-based classification techniques
Expert Systems with Applications: An International Journal
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
This paper introduces an accurate time---domain approach to model and classify the Malayalam consonant-Vowel (CV) speech unit waveforms. The technique is based on statistical models of Reconstructed State Space (RSS). A feature extraction method using RSS based State Space Point Distribution (SSPD) parameters are studied. The results of the simulation experiment performed on the Malayalam CV speech databases using Artificial Neural Network (ANN) and k-Nearest Neighborhood (k-NN) classifiers are also presented. The results indicate that the efficiency of the RSS approach is capable of increasing speaker independent consonant speech recognition accuracy.