On the use of a metric-space search algorithm (AESA) for fast DTW-based recognition of isolated words

The approximating and eliminating search algorithm (AESA) presented was recently introduced for finding nearest neighbors in metric spaces. Although the AESA was originally developed for reducing the time complexity of dynamic time-warping isolated word recognition (DTW-IWR), only rather limited experiments had been previously carried out to check its performance in this task. A set of experiments aimed at filling this gap is reported. The main results show that the important features reflected in previous simulation experiments are also true for real speech samples. With single-speaker dictionaries of up to 200 words, and for most of the different speech parameterizations, local metrics, and DTW productions tested, the AEAS consistently found the appropriate prototype while requiring only an average of 7-12 DTW computations (94-96% savings for 200 words), with a strong tendency to need fewer computations if the samples are close to their corresponding prototypes