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Abstract—This paper proposes two methods to incorporate semantic information into word and concept level confidence measurement. The first method uses tag and extension probabilities obtained from a statistical classer and parser. The second method uses a maximum entropy based semantic structured language model to assign probabilities to each word. Incorporation of semantic features into a lattice posterior probability based confidence measure provides significant improvements compared to posterior probability when used together in an air travel reservation task. At 5% False Alarm (FA) rate relative improvements of 28 % and 61 % in Correct Acceptance (CA) rate are achieved for word level and concept level confidence measurements, respectively. I.

We explore a novel approach for handwriting recognition tasks whose intrinsic vocabularies are too large to be applied directly as constraints during recognition. Our approach makes use of vocabulary constraints, and addresses the issue that some parts of words may be written more recognizably than others. An initial pass is made with an HMM recognizer, without vocabulary constraints, generating a lattice of character-hypothesis arcs representing likely segmentations of the handwriting signal. Arc confidence scores are computed using a posteriori probabilities. The most-confidently -recognized characters are used to filter the overall vocabulary, generating a word subset manageable for constraining a second recognition pass. With a vocabulary of 273,000 words, we can limit to 50,000 words in the second pass and eliminate 39.3% of the word errors made by a onepass recognizer without vocabulary constraints, and 18.3% of errors made using a fixed 30,000-word set.

We investigate use of large amounts, over 1500 hours, of untranscribed data recorded from a deployed conversational system to improve the acoustic and language models. The system that we considered allows users to perform transactions on their retirement accounts. Using all the untranscribed data we get over 19 % relative improvement in word error rate over a baseline system. In contrast, a system built using 70 hours of transcribed data results in over 31 % relative improvement. 1.