document: Word spotting based on a posterior measure of keyword confidence In this paper , an approach of keyword confidence estimation is developed that well combines acoustic layer scores and syllable-based statistical language model -LRB- LM -RRB- scores . An a posteriori -LRB- AP -RRB- confidence measure and its forward-backward calculating algorithm are deduced . A zero false alarm -LRB- ZFA -RRB- assumption is proposed for evaluating relative confidence measures by word spotting task . In a word spotting experiment with a vocabulary of 240 keywords , the keyword accuracy under the AP measure is above 94 % , which well approaches its theoretical upper limit . In addition , a syllable lattice Hidden Markov Model -LRB- SLHMM -RRB- is formulated and a unified view of confidence estimation , word spotting , optimal path search , and N-best syllable re-scoring is presented . The proposed AP measure can be easily applied to various speech recognition systems as well
doc_bio_tags: B I O O B I I O B I O O O O O O O B I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O B I O B I O B I I O O O O O O O O O O O O O O O O O O B I I O O
extractive_keyphrases: word spotting a posterior measure keyword confidence acoustic layer scores relative confidence measures syllable lattice hidden markov model confidence estimation optimal path search speech recognition systems
abstractive_keyphrases: syllable-based statistical language model scores a posteriori confidence measure forward-backward calculating algorithm zero false alarm assumption word spotting task n-best syllable re-scoring