A comparison of two LVR search optimization techniques

This paper presents a detailed comparison between two search optimization techniques for large vocabulary speech recognition – one based on word-conditioned tree search (WCTS) and one based on weighted finite-state transducers (WFSTs). Existing North American Business News systems from RWTH and AT&T representing each of the two approaches, were modified to remove variations in model data and acoustic likelihood computation. An experimental comparison showed that the WFST-based system explored fewer search states and had less runtime overhead than the WCTS-based system for a given word error rate. This is attributed to differences in the pre-compilation, degree of non-determinism, and path weight distribution in the respective search graphs.