ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
A segment-based interpretation of HMM/ANN hybrids
Computer Speech and Language
A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition
Pattern Recognition Letters
Robust ASR using Support Vector Machines
Speech Communication
Segmentation of specific speech signals from multi-dialog environment using SVM and wavelet
Pattern Recognition Letters
Synergy of Lip-Motion and Acoustic Features in Biometric Speech and Speaker Recognition
IEEE Transactions on Computers
Effective Detection of the Alzheimer Disease by Means of Coronal NMSE SVM Feature Classification
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
SVMs for automatic speech recognition: a survey
Progress in nonlinear speech processing
Expert Systems with Applications: An International Journal
Online signature verification with support vector machines based on LCSS kernel functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Penalized logistic regression with HMM log-likelihood regressors for speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Mathematical treatment of uncertainty in the speech recognition process
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
SVM-Enabled voice activity detection
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Speech event detection using support vector machines
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
An online algorithm for hierarchical phoneme classification
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
A speech recognizer based on multiclass SVMs with HMM-Guided segmentation
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
Efficient binary tree multiclass SVM using genetic algorithms for vowels recognition
CIMMACS'11/ISP'11 Proceedings of the 10th WSEAS international conference on Computational Intelligence, Man-Machine Systems and Cybernetics, and proceedings of the 10th WSEAS international conference on Information Security and Privacy
Speaker and digit recognition by audio-visual lip biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Information Sciences: an International Journal
Hi-index | 0.00 |
Support vector machines (SVMs) represent a new approach to pattern classification which has attracted a great deal of interest in the machine learning community. Their appeal lies in their strong connection to the underlying statistical learning theory, in particular the theory of structural risk minimization. SVMs have been shown to be particularly successful in fields such as image identification and face recognition; in many problems SVM classifiers have been shown to perform much better than other nonlinear classifiers such as artificial neural networks and k-nearest neighbors. This paper explores the issues involved in applying SVMs to phonetic classification as a first step to speech recognition. We present results on several standard vowel and phonetic classification tasks and show better performance than Gaussian mixture classifiers. We also present an analysis of the difficulties we foresee in applying SVMs to continuous speech recognition problems.