The final goal of this work is a Call-Recogniser, a device that identifies the meaning of vocal utterances of cows and presents the meaning to the farmer. Such a call-recogniser must be able to recognise the meaning of species-specific calls, independent from the individual animal and the probably more or less noisy environment.

As in speech recognition, the call recognition of animals can be regarded as a statistical paradigm. During the learning or training phase, feature vectors from known calls are calculated. From the feature vectors of calls with the same meaning, reference patterns are built and stored. For recognition, the feature vectors from an unknown call are calculated in the same way, and the system then determines the reference pattern that is most similar to the feature vector to be recognised, and outputs its meaning.

Despite the vocabulary size and complexity of human speech, which is unique in the animal realm, sound production and reception in vertebrates have much in common. This encourages, adaptation of methods and experiences from speech recognition to recognise animal calls. The problem of animal independent call recognition is comparable to speaker independent word spotting in speech recognition. In speech recognition, double stochastic processes, such as hidden Markov models (HMMs), have proved very efficient. They are applied here to recognise animal calls, using utterances of cows as an example. The results reveal that HMMs are well suited for animal call recognition.