Speaker-specific mapping for text-independent speaker recognition

Authors:
Hemant Misra;Shajith Ikbal;B. Yegnanarayana
Affiliations:
Department of Electrical Engineering, Indian Institute of Technology, Madras 600 036, India;Department of Computer Science and Engineering, Indian Institute of Technology, Madras 600 036, India;Department of Computer Science and Engineering, Indian Institute of Technology, Madras 600 036, India
Venue:
Speech Communication
Year:
2003

Citing 8
Cited 5

On the approximate realization of continuous mappings by neural networks

Neural Networks
Approximation capabilities of multilayer feedforward networks

Neural Networks
Fundamentals of speech recognition

Fundamentals of speech recognition
Speaker identification and verification using Gaussian mixture speaker models

Speech Communication
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Handset-Dependent Background Models for Robust Text-Independent Speaker Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Artificial Neural Networks

Artificial Neural Networks
Robust methods of updating model and a priori threshold in speaker verification

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01

Mapping speech spectra from throat microphone to close-speaking microphone: a neural network approach

EURASIP Journal on Advances in Signal Processing
Automated speech analysis applied to laryngeal disease categorization

Computer Methods and Programs in Biomedicine
An overview of text-independent speaker recognition: From features to supervectors

Speech Communication
Spectral mapping using artificial neural networks for voice conversion

IEEE Transactions on Audio, Speech, and Language Processing
Robust speaker identification in the presence of car noise

International Journal of Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present the concept of speaker-specific mapping for the task of speaker recognition. The speaker-specific mapping is realized using a multilayer feedforward neural network. In the mapping approach, the aim is to capture the speaker-specific information by mapping a set of parameter vectors specific to linguistic information in the speech, to a set of parameter vectors having linguistic and speaker information. In this study, parameter vectors suitable for speaker-specific mapping are explored. Background normalization for score comparison and network error criterion for frame selection are proposed to improve the performance of the basic system. It is shown that removing the high frequency components of speech results in loss of performance of the speaker verification system. For all the 630 speakers of the TIMIT database, an equal error rate (EER) of 0.5% and 100% identification is achieved by the mapping approach. On a set of 38 speakers of the dialect region "dr1" of NTIMIT database, an EER of 6.6% is obtained.