Channel variability synthesis in i-vector speaker recognition

Ahmed Isam Ahmed; John Chiverton; David Ndzi; Victor Becerra

doi:10.1049/cp.2017.0362

Channel variability synthesis in i-vector speaker recognition

Ahmed Isam Ahmed, John Chiverton, David Ndzi, Victor Becerra

Centre for Healthcare Modelling & Informatics

University of the West of Scotland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

In this paper, we are tackling a practical problem which can be faced when establishing an i-vector speaker recognition system with limited resources. This addresses the problem of lack of development data of multiple recordings for each speaker. When we only have one recording for each speaker in the development set, phonetic variability can be simply synthesised by dividing the recordings if they are of sufficient length. For channel variability, we pass the recordings through a Gaussian channel to produce another set of recordings, referred to here as Gaussian version recordings. The proposed method for channel variability synthesis produces total relative improvements in EER of 5%.

Original language	English
Title of host publication	IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)
Place of Publication	London, UK
Publisher	IET Conference Publications
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-78561-708-9
ISBN (Print)	978-1-78561-707-2
DOIs	https://doi.org/10.1049/cp.2017.0362
Publication status	Published - 21 May 2018
Event	IET 3rd International Conference on Intelligent Signal Processing (ISP 2017) - Savoy Place, IET Headquarters, London, United Kingdom Duration: 4 Dec 2017 → 5 Dec 2017 Conference number: 3 http://digital-library.theiet.org/content/conferences/cp731

Conference

Conference	IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)
Abbreviated title	IET ISP 2017
Country/Territory	United Kingdom
City	London
Period	4/12/17 → 5/12/17
Internet address	http://digital-library.theiet.org/content/conferences/cp731

Keywords

multi-condition training
i-vector
session variability

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10.1049/cp.2017.0362

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Accepted author manuscript (Post-print), 827 KB

Cite this

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title = "Channel variability synthesis in i-vector speaker recognition",

abstract = "In this paper, we are tackling a practical problem which can be faced when establishing an i-vector speaker recognition system with limited resources. This addresses the problem of lack of development data of multiple recordings for each speaker. When we only have one recording for each speaker in the development set, phonetic variability can be simply synthesised by dividing the recordings if they are of sufficient length. For channel variability, we pass the recordings through a Gaussian channel to produce another set of recordings, referred to here as Gaussian version recordings. The proposed method for channel variability synthesis produces total relative improvements in EER of 5%. ",

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author = "Ahmed, {Ahmed Isam} and John Chiverton and David Ndzi and Victor Becerra",

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Ahmed, AI, Chiverton, J , Ndzi, D & Becerra, V 2018, Channel variability synthesis in i-vector speaker recognition. in IET 3rd International Conference on Intelligent Signal Processing (ISP 2017). IET Conference Publications, London, UK, pp. 1-6, IET 3rd International Conference on Intelligent Signal Processing (ISP 2017), London, United Kingdom, 4/12/17. https://doi.org/10.1049/cp.2017.0362

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AU - Becerra, Victor

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KW - multi-condition training

KW - i-vector

KW - session variability

U2 - 10.1049/cp.2017.0362

DO - 10.1049/cp.2017.0362

M3 - Conference contribution

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BT - IET 3rd International Conference on Intelligent Signal Processing (ISP 2017)

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Y2 - 4 December 2017 through 5 December 2017

ER -

Channel variability synthesis in i-vector speaker recognition

Abstract

Conference

Keywords

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