Grant Title

Dr. Dolittle Project: A Framework for Classification and Understanding of Animal Vocalizations

Document Type


Publication Date


Source Publication

Journal of the Acoustical Society of America

Source ISSN



Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such methods, common in human speech recognition systems, create initial models trained on speaker independent data, then use small amounts of adaptation data to build individual-specific models. Since, as in human speech, individual vocal variability is a significant source of variation in bioacoustic data, acoustic model adaptation is naturally suited to classification in this domain as well. To demonstrate and evaluate the effectiveness of this approach, this paper presents the application of maximum likelihood linear regression adaptation to ortolan bunting (Emberiza hortulana L.) song-type classification. Classification accuracies for the adapted system are computed as a function of the amount of adaptation data and compared to caller-independent and caller-dependent systems. The experimental results indicate that given the same amount of data, supervised adaptation significantly outperforms both caller-independent and caller-dependent systems.

Document Rights and Citation of Original

Published version. Journal of the Acoustical Society of America, Vol. 123, No. 3 (March 2008): 1582–1590. DOI. © 2008 Acoustical Society of America. Used with permission.