A Large Czech Vocabulary Recognition System for Real-Time Applications

Abstract

In this paper, we propose two methods for speeding up discreteutterance recognition in vocabularies with hundreds to several thousands of words. We show that acceptable results as well as short response time can be achieved if the words are represented by concatenated monophone models (multi-mixture HMMs). In such case, the computation load of the classic Viterbi procedure can be reduced significantly if a proper caching scheme is used. In several experiments done with test vocabularies containing hundreds and thousands of Czech words, we demonstrate that the recognition procedures can be speeded up by a factor of 50 to 100 without a loss of accuracy. The method is well suited for voice controlled systems with a large branching factor and low syntax, i.e. in voice portals, telephone directory assistance, etc.KeywordsComputation LoadSpeech Recognition SystemCache SchemeContinuous Speech RecognitionContinuous Speech Recognition SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.