Publication

Speaker verification in score-ageing-quality classification space

Andrzej Drygajlo, Finnian Paul Kelly
2013
Journal Articles

Abstract

A challenge in automatic speaker verification is to create a system that is robust to the effects of vocal ageing. To observe the ageing effect, a speaker's voice must be analysed over a period of time, over which, variation in the quality of the voice samples is likely to be encountered. Thus, in dealing with the ageing problem, the related issue of quality must also be addressed. We present a solution to speaker verification across ageing by using a stacked classifier framework to combine ageing and quality information with the scores of a baseline classifier. In tandem, the Trinity College Dublin Speaker Ageing database of 18 speakers, each covering a 30-60 year time range, is presented. An evaluation of a baseline Gaussian Mixture Model Universal Background Model (GMM-UBM) system using this database demonstrates a progressive degradation in genuine speaker verification scores as ageing progresses. Consequently, applying a conventional threshold, determined using scores at the time of enrolment, results in poor long-term performance. The influence of quality on verification scores is investigated via a number of quality measures. Alongside established signal-based measures, a new model-based measure, Wnorm, is proposed, and its utility is demonstrated on the CSLU database. Combining ageing information with quality measures and the scores from the GMM-UBM system, a verification decision boundary is created in score-ageing-quality space. The best performance is achieved by using scores and ageing in conjunction with the new Wnorm quality measure, reducing verification error by 45% relative to the baseline. This work represents the first comprehensive analysis of speaker verification on a longitudinal speaker database and successfully addresses the associated variability from ageing and quality arte-facts. (C) 2013 Elsevier Ltd. All rights reserved.

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https://infoscience.epfl.ch/entities/publication/2c9bb446-9545-44d7-8999-8c6ddb5193ec

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Andrzej Drygajlo, Finnian Paul Kelly
2013
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2c9bb446-9545-44d7-8999-8c6ddb5193ec

About this result

Related concepts (15)

Quality (business)

In business, engineering, and manufacturing, quality – or high quality – has a pragmatic interpretation as the non-inferiority or superiority of something (goods or services); it is also defined as being suitable for the intended purpose (fitness for purpose) while satisfying customer expectations. Quality is a perceptual, conditional, and somewhat subjective attribute and may be understood differently by different people. Consumers may focus on the specification quality of a product/service, or how it compares to competitors in the marketplace.

Quality management

Quality management ensures that an organization, product or service consistently functions well. It has four main components: quality planning, quality assurance, quality control and quality improvement. Quality management is focused not only on product and service quality, but also on the means to achieve it. Quality management, therefore, uses quality assurance and control of processes as well as products to achieve more consistent quality. Quality control is also part of quality management.

Population ageing

Population ageing is an increasing median age in a population because of declining fertility rates and rising life expectancy. Most countries have rising life expectancy and an ageing population, trends that emerged first in developed countries but are now seen in virtually all developing countries. That is the case for every country in the world except the 18 countries designated as "demographic outliers" by the United Nations. The aged population is currently at its highest level in human history.

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