A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fundamentals of speech recognition
Fundamentals of speech recognition
What's in a number?: moving beyond the equal error rate
Speech Communication
Autoassociator-based models for speaker verification
Pattern Recognition Letters
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Handwritten Numerical Recognition Using Autoassociative Neural Networks
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Artificial Neural Networks
Speaker verification: minimizing the channel effects using autoassociative neural network models
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Multimodal person authentication using speech, face and visual speech
Computer Vision and Image Understanding
Speaker diarization using autoassociative neural networks
Engineering Applications of Artificial Intelligence
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
Voice conversion by mapping the speaker-specific features using pitch synchronous approach
Computer Speech and Language
Classification of audio signals using AANN and GMM
Applied Soft Computing
Pattern classification models for classifying and indexing audio signals
Engineering Applications of Artificial Intelligence
Recognition of emotions from video using neural network models
Expert Systems with Applications: An International Journal
Comparison of clustering methods: A case study of text-independent speaker modeling
Pattern Recognition Letters
ISCSLP SR evaluation, UVA–CS_es system description. a system based on ANNs
ISCSLP'06 Proceedings of the 5th international conference on Chinese Spoken Language Processing
Template matching approach for pose problem in face verification
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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
Score fusion in text-dependent speaker recognition systems
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
Emotion recognition from speech using source, system, and prosodic features
International Journal of Speech Technology
Film segmentation and indexing using autoassociative neural networks
International Journal of Speech Technology
Spoken keyword detection using autoassociative neural networks
International Journal of Speech Technology
Hi-index | 0.00 |
The objective in any pattern recognition problem is to capture the characteristics common to each class from feature vectors of the training data. While Gaussian mixture models appear to be general enough to characterize the distribution of the given data, the model is constrained by the fact that the shape of the components of the distribution is assumed to be Gaussian, and the number of mixtures are fixed a priori. In this context, we investigate the potential of non-linear models such as autoassociative neural network (AANN) models, which perform identity mapping of the input space. We show that the training error surface realized by the neural network model in the feature space is useful to study the characteristics of the distribution of the input data. We also propose a method of obtaining an error surface to match the distribution of the given data. The distribution capturing ability of AANN models is illustrated in the context of speaker verification.