With increasing sizes of speech databases, speech recognizers with huge parameter spaces have become trainable. However, the time and memory requirements for high accuracy realtime speaker-independent continuous speech recognition will probably not be met by the available hardware for a reasonable price for the next few years. This paper describes the application of the Bucket Voronoi Intersection algorithm to the JANUS-2 speech recognizer, which reduces the time for the computation of HMM emission probabilities with large Gaussian mixtures by 50% to 80%.
Bibliographic reference. Fritsch, J. / Rogina, I. / Sloboda, Tilo / Waibel, Alex (1995): "Speeding up the score computation of HMM speech regognizers with the bucket voronoi intersection algorithm", In EUROSPEECH-1995, 1091-1094.