Phone lattice reconstruction for embedded language recognition in LVCSR

An increasing number of multilingual applications require language recognition (LRE) as a frontend, but desire low additional computational cost. This article demonstrates a novel architecture for embedding phone based anguage recognition into a large vocabulary continuous speech recognition (LVCSR) decoder by sharing the same decoding process but generating separate lattices. To compensate for the prior bias introduced by the pronunciation dictionary and the language model of the LVCSR decoder, three different phone lattice reconstruction algorithms are proposed. The underlying goals of these algorithms are to override pronunciation and grammar restrictions to provide richer phonetic information. All of the new algorithms incorporate a vector space modeling backend for improved LRE accuracy. Evaluated on a Mandarin/English detection task, the proposed integrated LVCSR-LRE system using frame-expanded N-best phone lattice achieves comparable performance to a state-of-the-art phone recognition-vector space modeling (PRVSM) system, but with an added computational cost three times lower than that of a separate PRVSM system.