Open Access

Pitch Correlogram Clustering for Fast Speaker Identification

EURASIP Journal on Advances in Signal Processing20042004:372807

DOI: 10.1155/S1110865704408026

Received: 24 June 2003

Published: 27 December 2004


Gaussian mixture models (GMMs) are commonly used in text-independent speaker identification systems. However, for large speaker databases, their high computational run-time limits their use in online or real-time speaker identification situations. Two-stage identification systems, in which the database is partitioned into clusters based on some proximity criteria and only a single-cluster GMM is run in every test, have been suggested in literature to speed up the identification process. However, most clustering algorithms used have shown limited success, apparently because the clustering and GMM feature spaces used are derived from similar speech characteristics. This paper presents a new clustering approach based on the concept of a pitch correlogram that captures frame-to-frame pitch variations of a speaker rather than short-time spectral characteristics like cepstral coefficient, spectral slopes, and so forth. The effectiveness of this two-stage identification process is demonstrated on the IVIE corpus of 110 speakers. The overall system achieves a run-time advantage of 500% as well as a 10% reduction of error in overall speaker identification.

Keywords and phrases

speaker identification clustering pitch correlogram

Authors’ Affiliations

Research and Development Division, Danlaw Technologies India Limited
Department of Electrical and Electronic Engineering, The University of Melbourne


© Jhanwar and Raina 2004