An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Keyword Spotting Using Support Vector Machines
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Improved Algorithm for Kernel Principal Component Analysis
Neural Processing Letters
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
A Speech Recognition System Based on a Hybrid HMM/SVM Architecture
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Kernel based Non-linear Feature Extraction Methods for Speech Recognition
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
A fast kernel-based nonlinear discriminant analysis for multi-class problems
Pattern Recognition
A comparison of grapheme and phoneme-based units for Spanish spoken term detection
Speech Communication
Discriminative keyword spotting
Speech Communication
Posterior-based confidence measures for spoken term detection
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods
SVMs for automatic speech recognition: a survey
Progress in nonlinear speech processing
An application of recurrent neural networks to discriminative keyword spotting
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Theory and Applications of Digital Speech Processing
Theory and Applications of Digital Speech Processing
Expert Systems with Applications: An International Journal
Learning Linear and Nonlinear PCA with Linear Programming
Neural Processing Letters
An online algorithm for hierarchical phoneme classification
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
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A keyword spotter is considered as a binary classifier that separates a class of utterances containing a target keyword from utterances without the keyword. These two classes are not inherently linearly separable. Thus, linear classifiers are not completely suitable for such cases. In this paper, we extend a kernel-based classification approach to separate the mentioned two non-linearly separable classes so that the area under the Receiver/Relative Operating Characteristic (ROC) curve (the most common measure for keyword spotter evaluation) is maximized. We evaluated the proposed keyword spotter under different experimental conditions on TIMIT database. The results indicate that, in false alarm per keyword per hour smaller than two, the true detection rate of the proposed kernel-based classification approach is about 15 % greater than that of the linear classifiers exploited in previous researches. Additionally, area under the ROC curve (AUC) of the proposed method is 1 % higher than AUC of the linear classifiers that is significant due to confidence levels 80 and 95 % obtained by t-test and F-test evaluations, respectively. In addition, we evaluated the proposed method in different noisy conditions. The results indicate that the proposed method show a good robustness in noisy conditions.