원문정보
초록
영어
This paper presents a statistical learning algorithm based on Support Vector Machines (SVMs) for the classification of Malayalam Consonant – Vowel (CV) speech unit in noisy environments. We extend SVM for multiclass classification using Decision Directed Acyclic Graph Support Vector Machine (DDAGSVM) algorithm. For classification, acoustical features are extracted using Wavelet Transform (WT) based Normalized Wavelet Hybrid Features (NWHF) by combining both Classical Wavelet Decomposition (CWD) and Wavelet Packet Decomposition (WPD) along with z – score normalization. An optimum mother wavelet for the present speech database is selected as db2 by trial and error approach. The classification results are then compared with both Artificial Neural Networks (ANNs) and k – Nearest Neighborhood (k – NN) classifiers. The results indicate that the DDAGSVM algorithm perform well in additive noisy condition.
목차
1. Introduction
2. Wavelet Transform
3. Multi Resolution Analysis and Subband Coding
4. Wavelet Decompositions
4.1. Classical Wavelet Decomposition
4.2. Wavelet Packet Decomposition
5. Normalized Wavelet Hybrid Feature Extraction
6. Classification
6.1. k – Nearest Neighborhood
6.2. Artificial Neural Network
6.3. Support Vector Machine
7. Simulation Experiments and Results
8. Conclusion
References