A. Ariyaeeinia

University of Hertfordshire

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P. Sivakumaran

University of Hertfordshire

A. Malegaonkar

University of Hertfordshire

J. Fortuna

McMaster University

M. Pawlewski

BT Research

S. Pillay

RELX Group (Netherlands)

Fawaz Waselallah Alsaade

King Faisal University

Jill Hewitt

University of Hertfordshire

Reza Sotudeh

University of Hertfordshire

Zainal Ahmad

Universiti Teknologi Petronas

MJ LOOMES

University of Hertfordshire

Cooperative Institutions

University of Hertfordshire

London South Bank University

Brunel University of London

Universiti Teknologi Petronas

International Islamic University College Selangor

Universiti Sains Malaysia

Sunan Kalijaga State Islamic University Yogyakarta

Newcastle University

Hospital Universiti Sains Malaysia

University of Newcastle Australia

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Effectiveness of orthogonal instantaneous and transitional feature parameters for speaker verification

A. Ariyaeeinia P. Sivakumaran

The effectiveness, for text-dependent speaker verification, of orthogonal instantaneous and transitional feature parameters of speech is investigated. Instantaneous spectral features are represented by cepstral coefficients obtained through a linear prediction analysis of speech. Transitional spectral information is characterised using differential cepstral coefficients. Sets of orthogonal parameters are obtained by applying an eigenvector analysis to instantaneous and transitional feature coefficients. The experimental work is based on the use of a subset of the BT Millar speech database, consisting of repetitions of isolated digit utterances 1 to 9 and zero spoken by twenty male speakers. The investigation includes an examination of the relative speaker discrimination abilities of the above two types of orthogonal feature parameters. It is shown experimentally that the equal error rate in verification can be reduced significantly by forming a spectral distance based on a combination of orthogonal instantaneous and transitional feature parameters. It is further demonstrated that, when the input utterance consists of a sequence of five digits, an equal error rate of less than 0.5% can be achieved.

Feature (linguistics)

Mel-frequency cepstrum

Cepstrum

Sequence (biology)

Word error rate

Linear prediction

Utterance

Speaker Verification

10.1109/ccst.1995.524737

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Citations (2)

On the use of the Bayesian information criterion in multiple speaker detection

P. Sivakumaran J. Fortuna A. Ariyaeeinia

An efficient scheme, based on the Bayesian information criterion (BIC), for the detection of speaker changes in an audio stream is introduced and investigated. BIC has been the subject of considerable attention in recent years due to its effectiveness for speaker change detection (SCD) as well as the detection of other forms of acoustic changes. A main difficulty in BIC-based SCD has been reported to be that of the computational complexity. The scheme proposed here tackles this problem by reducing the computational load in the previously proposed algorithms significantly, without compromising their effectiveness. The paper describes the new scheme thoroughly and analyses its performance. Experiments are based on 3 hours of broadcast news with 416 speaker changes. With this data, the proposed scheme has been found to be capable of running in about 0.06 times real-time whilst keeping the rate of each of misdetection and false alarm close to 9%.

Bayesian information criterion

False alarm

Speaker diarisation

10.21437/eurospeech.2001-248

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Citations (28)

Effective speaker verification via dynamic mismatch compensation

IET Biometrics (2012)

S. Pillay A. Ariyaeeinia P. Sivakumaran M. Pawlewski

This paper presents a new approach to condition-adjusted T-norm (CT-Norm) for speaker verification under significant mismatched noise conditions. The study is motivated by the fact that, though the standard CT-Norm method offers enhanced accuracy under mismatched data conditions, its effectiveness reduces with the increased severity of such conditions. The proposed approach attempts to address this challenge by providing a more effective reduction of data mismatch through the incorporation of multi-signal-to-noise ratio (SNR) universal background models (UBMs). The effectiveness of the proposed approach is demonstrated through experiments based on examples of real-world noise. It is shown that the superiority of the approach over CT-Norm is particularly significant for such excessive levels of test data degradation considered in the study as 5 dB SNR and below. The paper provides a description of the characteristics of the proposed approach and details the experimental analysis of its effectiveness under different noise conditions.

Speaker Verification

10.1049/iet-bmt.2012.0001

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Citations (3)

AN EFFECTIVE SUB-BAND BASED APPROACH FOR ROBUST SPEAKER VERIFICATION

P. Sivakumaran A. Ariyaeeinia J. Hewitt JA MALCOLM

INTRODUCI ION

Speaker Verification

10.25144/18982

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Citations (0)

Unsupervised speaker change detection using probabilistic pattern matching

IEEE Signal Processing Letters (2006)

A. Malegaonkar A. Ariyaeeinia P. Sivakumaran J. Fortuna

This letter presents an investigation into the use of a probabilistic pattern matching approach for detecting speaker changes in audio streams. The experiments are conducted using clean speech as well as broadcast news material. It is shown that, in the proposed approach, the use of bilateral scoring is considerably more effective than unilateral scoring. Appropriate score normalization methods are considered in the study. It is observed that in all the cases, the bilateral scoring approach outperforms the currently popular method of Bayesian information criterion (BIC) for speaker change detection. This letter discusses the principles of the proposed approach and details the experimental investigations

Normalization

10.1109/lsp.2006.873656

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Citations (25)

Editorial: Biometric recognition

IET Signal Processing (2009)

A. Ariyaeeinia

Over the last decade, biometrics has become the subject of growing attention as a promising technology for complementing, or even replacing, the traditional means of verifying individuals' identities in a variety of applications. These include certain specific Government applications such as border control and security, as well as applications in other areas such as finance, commerce and health services. The idea has been primarily driven by the inherent limitations of the traditional identity verification methods, and is supported through accelerated research into different aspects of the technology. In general, an additional benefit of biometrics is that it provides a valid basis for the identification process. This considerably increases the attraction of the technology and its potential value in various practical scenarios.

Identification

10.1049/iet-spr.2009.9038

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Citations (0)

Fusion of cross stream information in speaker verification

Fawaz Waselallah Alsaade A. Malegaonkar A. Ariyaeeinia

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiments conducted using a GMM-based speaker verification system, provides valuable information on the relative effectiveness of different fusion methods applied at the score level. It is also demonstrated that a higher speaker discrimination capability can be achieved by applying the fusion at the score level rather than at the feature level.

Mel-frequency cepstrum

Cepstrum

Speaker Verification

Feature (linguistics)

Sensor Fusion

Speaker diarisation

Source

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Citations (3)

An effective unsupervised scheme for multiple-speaker-change detection

4th International Conference on Spoken Language Processing (ICSLP 1996) (2002)

P. Sivakumaran A. Ariyaeeinia J. Fortuna