Fusing language identification systems using performance confidence indexes

In the field of automatic language identification, several, mostly-empirical, arithmetic fusion operations are currently done to make a consensus decision from a set of acoustics-based identification systems whose estimated performance is taken into account by means of weighting techniques. The paper presents how to apply the discriminant factor analysis method formally to compute and use weighting performance confidence indexes at the expert and class levels. Moreover, the observation level is also explored. These confidence indexes allow us not only to qualify the identification decision with additional insight by means of a certainty degree, but also to provide acoustics-based identification systems with powerful uncertainty-based inference techniques where the systems´ a priori performance knowledge is a key heuristic-like element to improve language identification capabilities.