Fast likelihood computation methods for continuous mixture densities in large vocabulary speech recognition
Stefan Ortmanns, Thorsten Firzlaff, Hermann Ney · 1997
This paper studies algorithms for reducing the computational eort of the mixture density calculations in HMM-based speech recognition systems.These likelihood calculations take about 70 85% of the total recognition time in the RWTH system for large vocabulary continuous speech recognition.To reduce the computational cost of the likelihood calculations, we investigate several space partitioning methods.A detailed comparison of these techniques is given on the North American Business Corpus (NAB'94) for a 20 000word task.As a result, the so-called projection search algorithm in combination with the VQ method reduces the cost of likelihood computation by a factor of about 8 with no signi cant loss in the word recognition accuracy.