Fundamentals of speech recognition
Fundamentals of speech recognition
Pairwise classification and support vector machines
Advances in kernel methods
Handbook of Neural Networks for Speech Processing
Handbook of Neural Networks for Speech Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Large vocabulary continuous speech recognition of Broadcast News - The Philips/RWTH approach
Speech Communication - Special issue on automatic transcription of broadcast news data
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support vector machines for speech recognition
Support vector machines for speech recognition
Artificial Neural Networks
On the use of support vector machines for phonetic classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Spotting multilingual consonant-vowel units of speech using neural network models
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
| Hi-index | 0.00 |
This paper addresses the issues in recognition of the large number of subword units of speech using support vector machines (SVMs). In conventional approaches for multi-class pattern recognition using SVMs, learning involves discrimination of each class against all the other classes. We propose a close-class-set discrimination method suitable for large-class-set pattern recognition problems. In the proposed method, learning involves discrimination of each class against a subset of classes confusable with it and included in its close-class-set. We study the effectiveness of the proposed method in reducing the complexity of multi-class pattern recognition systems based on the one-against-the rest and one-against-one approaches. We discuss the effects of symmetry and uniformity in size of the close-class-sets on the performance for these approaches. We present our studies on recognition of 86 frequently occurring Consonant-Vowel units in a continuous speech database of broadcast news.