Abstract
Experiments and results of the application of the Approximating and Eliminating Search Algorithm ( aesa) to multi-speaker data are reported. Previous (single-speaker) results had already shown that the performance (speed) of the aesa remains greatly insensitive to increasing the size of the dictionary, while a very strong (exponential)_tendency to higher performance is exhibited as the test utterances are close to their corresponding prototypes. Following these results we show in this paper that, by increasing the number of tokens included in dictionaries with multiply represented words, a simultaneous reduction can be achieved in both the error-rate and the number of distance computations required. The speech data used in the experiments corresponds to the Spanish digit vocabulary uttered several times by 10 different male and female speakers, and it has been found that very accurate (> 99%) recognition of this vocabulary can be achieved while requiring only about 5 DTW computations on the average.
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