COMPARISON OF CLUSTERING ALGORITHMS IN SPEAKER IDENTIFICATION

Abstract

In speaker identification, we match a given (unkown) speaker to the set of known speakers in a database. The database is constructed from the speech samples of each known speaker. Feature vectors are extracted from the samples by short-term spectral analysis, and processed further by vector quantization for locating the clusters in the feature space. We study the role of the vector quantization in the speaker identification system. We compare the performance of different clustering algorithms, and the influence of the codebook size. We want to find out, which method provides the best clustering result, and whether the difference in quality contribute to improvement in recognition accuracy of the system.