Abstract

Our first application of weighted finite state transducers to the recognition of broadcast news provided us with an interesting framework to study several problems related to the optimization of the search space. The paper starts by describing how the use of our lexicon and language model "on-the-fly" composition algorithm is crucial in extending the transducer approach to large systems. We present an efficient representation for WFSTs, that allowed us to reduce runtime memory requirements, and discuss several types of language model optimizations, including a context-sharing algorithm. Experimental results obtained with the broadcast news corpus collected for European Portuguese illustrate the impact of the various possible optimizations of the components on the performance of the system.

Citations