Mubeen N

Shahina A

Khan A.N

Vinoth G.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Combining spectral features of standard and Throat Microphones for speaker identification

2012 International Conference on Recent Trends in Information Technology

A review on multimodal speaker recognition (SR) is being presented. For many decades the speaker recognition has been studied and still it has grabbed the interest of many researchers. Speaker recognition includes of two levels –system training and system testing. The robustness of the speaker recognition system depends on the training environment and testing environment as well as the quality of speech .Air conducted (AC) Speech is a source from which speaker is recognized by extracting the features. The performance of the speaker recognition system depends on AC speech. further to improve the robustness and accuracy of the SR system various other sources(Modals) like Throat Microphone ,Bone Conduction Microphone, array of microphones,Non Audible murmur, non auditory information like video are used in complementary with standard AC microphone. This paper is purely a review on SR and various complimentary modals.

Fulltext

Asian Journal of Pharmaceutical and Clinical Research

A REVIEW ON MULTIMODAL SPEAKER RECOGNITION

Automatic identification of a language in a noisy environment is a challenging task. Performance of a language identification system depends on the quality of the input speech signal. In the presence of high levels of background noise, speech signals recorded using a close-speaking microphone are degraded. In contrast, a throat microphone picks up high quality speech unaffected by the surrounding noise. This paper explores the possibility of using throat microphone speech signals for text-independent language identification in noisy conditions. Languages are modelled using autoassociative neural networks based on the vocal tract system features and excitation source features derived from the throat speech signal. The results of this study show that the throat microphone speech-based language identification system performs well in noisy environments. ©2005 IEEE.

Proceedings - 2005 International Conference on Intelligent Sensing and Information Processing, ICISIP'05

Language identification in noisy environments using throat microphone signals

IEEE Signal Processing Letters

Combining standard and throat microphones for robust speech recognition

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. © 2002 Elsevier Science B.V. All rights reserved.

Speech Communication

Speaker-specific mapping for text-independent speaker recognition

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.

Neural Networks

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.

Journal	Data powered by Typeset2012 International Conference on Recent Trends in Information Technology
Publisher	Data powered by TypesetIEEE
Open Access	0