Partial speech sentence matching for personal calendar information retrieval

The paper describes a new partial matching algorithm to retrieve speech information from a speech database by spoken sentence query. The similarity between two partial matching sentences is evaluated by a new algorithm called column-based row-based (CBRB) evaluation. This feature-matching method does not need a complex language model, so the system is language independent. Moreover, the proposed approach does not involve a large-vocabulary recognizer, which can heavily load a system, so the proposed approach greatly reduces the computational burden, and is highly appropriate for handheld devices. Finally, experiments were conducted for a personal speech calendar and the results show that our system can successfully evaluate the similarity between database sentences and a query sentence.