## Third International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2003)## Florence, Italy |

In this paper, we purpose a theoretical development of a metric for speech classification based on cepstral features obtained from ARMA models. Thus working with an ARMA model as a complex rational function, is possible to define a metric d(M,M´) between two stable ARMA models M, M´ by means of the cepstrum coefficients of the models. This metric may be calculated algorithmically as a finite sum in the pole-zero domain. We suggest that the metric can be used in at least two circumstances: first, we might a large number of signals that come from various types of pathological sources and we wish to classify them; alternatively, we might the underlying models Mi corresponding to several pathological voices and we wish to classify a voice (modeled as M, say) from one of those. In that case, we compute d(M,Mi) for each i and we guess the (Mi) closest to the model M.

*Index Terms*. ARMA model, cepstrum, distance
measure, classification, pathological voice

Full Paper (reprinted with permission from Firenze University Press)

__Bibliographic reference.__
Martínez, F. / Guillamón, A. / Martínez, J. J. (2003):
"A suggested metric for cepstral ARMA-based speech classification",
In *MAVEBA-2003*, 165-168.