FST-based recognition techniques for multi-lingual and multi-domain spontaneous speech

In this paper we present techniques for building multi-domain and multi-lingual recognizers within a finite-state transducer (FST) framework. The flexibility of the FST approach is also demonstrated on the task of incorporating networks modeling different types of non-speech events into an existing word lattice network. The ability to create robust multi-domain and/or multi-lingual recognizers for spontaneous speech will enable a conversational system to switch seamlessly and automatically among different domains and/or languages. Preliminary results using a bi-domain recognizer exhibit only small recognition accuracy degradation in comparison to domain-dependent recognition. Similarly promising results were observed using a bilingual recognizer which performs simultaneous language identification and recognition. When using the FST techniques to add non-speech models to the recognizer, experiments show a 10% reduction in word error rate across all utterances and a 30% reduction on utterances containing non-speech events.