Document Type

Article

Language

eng

Format of Original

8 p.

Publication Date

2-2005

Publisher

Acoustical Society of America

Source Publication

Journal of the Acoustical Society of America

Source ISSN

0001-4966

Original Item ID

doi: 10.1121/1.1847850

Abstract

A hidden Markov model (HMM) system is presented for automatically classifying African elephant vocalizations. The development of the system is motivated by successful models from human speech analysis and recognition. Classification features include frequency-shifted Mel-frequency cepstral coefficients (MFCCs) and log energy, spectrally motivated features which are commonly used in human speech processing. Experiments, including vocalization type classification and speaker identification, are performed on vocalizations collected from captive elephants in a naturalistic environment. The system classified vocalizations with accuracies of 94.3% and 82.5% for type classification and speaker identification classification experiments, respectively. Classification accuracy, statistical significance tests on the model parameters, and qualitative analysis support the effectiveness and robustness of this approach for vocalization analysis in nonhuman species.

Comments

Published version. Journal of the Acoustical Society of America, Vol. 117, No. 2 (February 2005): 956-963. DOI. © Acoustical Society of America 2005. Used with permission.

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