Speaker recognition is used to identify individual humans, but has rarely been applied to other species. To be applicable to the wide variety of bird species, text-independent speaker identification would be the most effective method. This is the first paper to report results of this technique in a species other than humans. Mel-frequency cepstral coefficients were extracted from recordings of three bird species and a multilayer perceptron was used as the classifier in each species. First, the song types used in training and testing were not controlled for, and these conditions gave an accuracy of 68-100%. Next the recordings of the wagtails and scrub-birds were split into their respective song types, a network was trained with one song type from each individual and tested with a different song type. With these purely text-independent conditions the accuracy was 71-96%.
Cite as: Fox, E.J.S., Roberts, J.D., Bennamoun, M. (2006) Text-independent speaker identification in birds. Proc. Interspeech 2006, paper 1068-Wed3CaP.12, doi: 10.21437/Interspeech.2006-196
@inproceedings{fox06_interspeech,
author={E. J. S. Fox and J. D. Roberts and M. Bennamoun},
title={{Text-independent speaker identification in birds}},
year=2006,
booktitle={Proc. Interspeech 2006},
pages={paper 1068-Wed3CaP.12},
doi={10.21437/Interspeech.2006-196}
}