High speed speech recognition using tree-structured probability density function

Abstract

This paper proposes a new speech recognition method using a tree-structured probability density function (PDF) to realize high speed HMM based speech recognition. In order to reduce the likelihood calculation for a PDF set composed of the Gaussian PDFs for all mixture components, all states and all recognition units, it is coarsely done for the element PDF whose likelihood is not likely to be large. The PDF set is expressed as a tree-structured form. In the recognition process, the likelihood set is calculated by searching the tree; by calculating the likelihood from the cluster PDF at the node and traversing the nodes with the largest likelihood from the root. Experimental results showed that the computation load was drastically reduced with little reduction in the recognition accuracy, in both speaker-independent and speaker-adaptive cases. The algorithm was applied to a personal computer speech recognition software without using special hardware.