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Automatic age recognition, call-type classification, and speaker identification of Zebra Finches (Taeniopygia guttata) using hidden Markov models (HMMs)

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Abstract

Hidden Markov models (HMMs) were developed and implemented to discriminate between each of the 2 ages, 11 call-types, and 51 speakers of birds using cross-validation on the recordings in the 3314 database for chick (19–25 days of age) and adult (60 days–7 years of age) vocalizations of Zebra Finches (Taeniopygia guttata). By applying both temporal [delta (velocity) and delta-delta (acceleration) coefficients] and spectral [Mel-Frequency Cepstral Coefficients (MFCCs)] features, the HMMs produced excellent performance with accuracies on the three tasks: (1) 96.68% (age recognition); (2) 94.62% (chicks) and 79.30% (adults) (call-type classification); and (3) 55.32% (12 speakers, chicks) and 16.78% (33 speakers, adults) to 100.00% (2 speakers, chicks), and 100.00% (3 speakers adults) (speaker identification). Based on the performances, the HMMs could be extended to other animals for automatic recognition, classification, and identification tasks.

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  1. Marquette University, 1313 W. Wisconsin Avenue, Milwaukee, WI, 53233, USA

    Marek B. Trawicki

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  1. Marek B. Trawicki
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Trawicki, M.B. Automatic age recognition, call-type classification, and speaker identification of Zebra Finches (Taeniopygia guttata) using hidden Markov models (HMMs). Int J Speech Technol 26, 641–650 (2023). https://doi.org/10.1007/s10772-023-10041-0

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