МЕТОД ОБЪЕДИНЕНИЯ РЕШЕНИЙ КЛАССИФИКАТОРОВ ДЛЯ ЗАДАЧИ РАСПОЗНАВАНИЯ ДИКТОРА

The paper proposes using fuzzy integrals for merging classifier decisions in speaker recognitionsystems. Instantaneous frequency and instantaneous amplitude are considered as the set of features. The approach shows significantly better results than a single classifier. A comparison of the proposed approach with the other methods for merging classifier decisions is provided.Предлагается использование нечетких интегралов для объединения решений классификаторовсистем распознавания диктора. В качестве набора признаков рассматриваются мгновенная частота и мгновенная амплитуда. Предлагаемый метод показывает значительно лучшие результаты по сравнению с применением единственного классификатора. Проводится сравнение предлагаемого метода с другими методами объединения решений классификаторов

Speaker Verification With Low Storage Requirements

As speaker verification continues to gain acceptance for security applications, the day is drawing near when millions of users worldwide will be using some form of speaker verification system to gain access to secure information. These accesses will most probably be via the telephone network. Within this scenario a huge data flow across long distances is expected, in order to have the speaker's templates available for verification. This paper presents initial work on reducing the data requirements for text-dependent speaker verification. It is shown how data storage and accuracy can be traded off in such systems. 1. INTRODUCTION In this paper we address the issue of data reduction for text dependent speaker verification. Although this work concentrates on text dependent speaker verification, the philosophy of data reduction for speaker verification is relevant for all verification algorithms which have a large (order of hundreds of thousands and more) population of speakers. Given the..