A study on improving decisions in closed set speaker identification

In this study, closed set, text-independent speaker identification is considered and the problem of improving the reliability of the decisions made by available algorithms is addressed. The work presented is based on the idea of combining the evidence from different algorithms or decision strategies to improve the recognition performance and the reliability. For this purpose, the models generated by a single algorithm for 17 speakers from the SPIDRE database are considered and a matrix of speaker-to-model fitness values is processed by two different decision strategies. Ideas from the mathematical theory of evidence are applied to combine the decisions produced by these two strategies to generate a better decision on the speaker identity. The combined decision show an improved degree of correctness hence suggesting a promising way of combining the decisions from partially successful algorithms.