Abstract
Modeling a system by statistical methods needs large amount of data to train the system. In real life, such data are sometimes not available or hard to collect. Modeling the system with small size database will produce a system with poor performance. In this paper, a method for increasing the size of a speech database is proposed. The method works by generating new samples from the original samples, using combinations of the following methods: speech lengthening, noise addition, and word reversal. To make a proof of concept, a severe test condition is used, in which the original database consists of one sample per speaker, for a speaker recognition system. The system is tested using original samples and the highest obtained recognition rate for mixed genders is 91.41% and that of 93.24% for male only speakers. Key words: Lengthening of samples, noise addition, word reversal, speaker recognition.
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