Distributed Chinese Keyw Verification for Spoken Wireless Envir

With the rapid developments of wireless communications, it is highly desired for users to access the network information with spoken dialogue interface via hand-held devices at any time, from anywhere. One possible approach towards this goal is to perform speech feature extraction at the hand-held devices (the clients) and have all other recognition tasks and dialogue functions absorbed by the server. This paper investigated distributed Chinese keyword spotting and verification under this scenario. A “phonetically distributed” Mandarin speech database including all possible Mandarin syllables and context relationships with frequencies roughly proportional to those occurring in daily Mandarin conversation is used to train a best set of vector quantization codebooks, such that the syllable recognition accuracy degradation due to quantization errors is minimized. Enhanced Chinese keyword spotting techniques were then developed using utterance verification approaches with weighting parameters optimized by MCE training. Experimental results indicated that the keyword verification approach achieved significant improvements in keyword spotting performance, and the overall results integrating vector quantization, keyword spotting and verification is quite satisfactory.