The characteristics of the telephone channel and handset have a significant effect on the performance of speaker verification systems. The channel/handset mismatch between the training and testing data degrades the performance of speaker verification systems. In this paper, we show that the autoassociative neural network (AANN) models can be used to minimize the effects of channel characteristics on the performance of a text-independent speaker verification system. This paper also compares two approaches to represent the background model for an AANN based speaker verification system. © 2000 IEEE.

S. P. Kishore

Neural Networks

signal processing

Telephone sets

Bandwidth

Channel Capacity

Database systems

Feedforward neural networks

Mathematical models

AUTOASSOCIATIVE NEURAL NETWORKS

Background model

CHANNEL CHARACTERISTICS

CHANNEL EFFECT

SPEAKER VERIFICATION

SPEAKER VERIFICATION SYSTEM

TEXT-INDEPENDENT SPEAKER VERIFICATION

TRAINING AND TESTING

Speech Recognition

AUTOASSOCIATIVE NEURAL NETWORK

SPEAKER VERIFICATION SYSTEMS

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Speaker verification: minimizing the channel effects using autoassociative neural network models

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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. © 2002 Published by Elsevier Science Ltd.

AANN: An alternative to GMM for pattern recognition

In this paper we study the effectiveness of the features extracted from the source and system components of speech production process for the purpose of speaker recognition. The source and system components are derived using linear prediction (LP) analysis of short segments of speech. The source component is the LP residual derived from the signal, and the system component is a set of weighted linear prediction cepstral coefficients. The features are captured implicitly by a feedforward autoassociative neural network (AANN). Two separate speaker models are derived by training two AANN models using feature vectors corresponding to source and system components. A speaker recognition system for 20 speakers is built and tested using both the models to evaluate the performance of source and system features. The study demonstrates the complementary nature of the two components.

Source and system features for speaker recognition using AANN models

JOURNAL OF ELECTRONICS INFORMATION & TECHNOLOGY

Speaker Identification Based on GMM with Embedded AANN

2006 IEEE Odyssey - The Speaker and Language Recognition Workshop

NIST Speaker Recognition Evaluation Chronicles - Part 2

Neural Smithing

Genetic Algorithms and Neural Networks

IEEE Transactions on Acoustics, Speech, and Signal Processing

Cepstral analysis technique for automatic speaker verification

Speaker recognition using orthogonal linear prediction

Speaker verification: Minimizing the channel effects using autoassociative neural network models

Journal	Data powered by TypesetICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN	15206149
Open Access	No