Speaker verification with long-term ageing data

The change experienced by the voice due to ageing must be considered in the development of a long-term speaker verification system. This difficult, largely open, research problem has received little attention to date. For this study, a new Speaker Ageing Database has been collected, containing speech from 18 speakers over a 30-60 year time span. A speaker verification evaluation of this data with a Gaussian Mixture Model - Universal Background Model system reveals that the verification scores of genuine speakers decrease progressively as the time span between training and testing increases, while the imposter scores are less affected. As a consequence, applying a decision threshold fixed at time of enrolment results in a high classification error rate after only a few years. A stacked classifier method of introducing an ageing-dependent decision boundary is applied, significantly improving long-term verification accuracy. Due to score variability at extended time spans however, accurate classification remains a challenging research problem. The ageing-dependent classification approach introduced here represents a first step towards dealing with long-term ageing in speaker verification systems.