Open-vocabulary keyword detection from super-large scale speech database

This paper presents our recent attempt to make a super-large scale spoken-term detection system, which can detect any keyword uttered in a 2,000-hour speech database within a few seconds. There are three problems to achieve such a system. The system must be able to detect out-of-vocabulary (OOV) terms (OOV problem). The system has to respond to the user quickly without sacrificing search accuracy (search speed and accuracy problem). The pre-stored index database should be sufficiently small (index size problem). We introduced a phoneme-based search method to detect the OOV terms, and combined it with the LVCSR-based method. To search for a keyword from large-scale speech databases accurately and quickly, we introduced a multistage rescoring strategy which uses several search methods to reduce the search space in a stepwise fashion. Furthermore, we constructed an out-of-vocabulary/in-vocabulary region classifier, which allows us to reduce the size of the index database for OOVs. We describe the prototype system and present some evaluation results.