Large Vocabulary Search Space Reduction Employing Directed Acyclic Word Graphs and Phonological Rules

Abstract

Some applications of speech recognition, such as automatic directory information services, require very large vocabularies. In this paper, we focus on the task of recognizing surnames in an Interactive telephone-based Directory Assistance Services (IDAS) system, which supersedes other large vocabulary applications in terms of complexity and vocabulary size. We present a method for building compact networks in order to reduce the search space in very large vocabularies using Directed Acyclic Word Graphs (DAWGs). Furthermore, trees, graphs and full-forms (whole words with no merging of nodes) are compared in a straightforward way under the same conditions, using the same decoder and the same vocabularies. Experimental results showed that, as we move from full-form lexicons to trees and then to graphs, the size of the recognition network is reduced, as is the recognition time. However, recognition accuracy is retained since the same phoneme combinations are involved. Subsequently, we refine the N-best hypotheses' list provided by the speech recognizer by applying context-dependent phonological rules. Thus, a small number N in the N-best hypotheses' list produces multiple solutions sufficient to retain high accuracy and at the same time achieve real-time response. Recognition tests with a vocabulary of 88,000 surnames that correspond to 123,313 distinct pronunciations proved the efficiency of the approach. For N = 3 (a value that ensures we have fast performance), before the application of rules the recognition accuracy was 70.27%. After applying phonological rules the recognition performance rose to 86.75%.