Automatic editing using probabilistic word substitution models

Details Automatic editing using probabilistic word substitution models Automatic editing using probabilistic word substitution models

2007-03-30

2010-10-12

Azad, Abul (Department: 2626)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S235000

Reexamination Certificate

active

07813929

ABSTRACT:
An input sequence of unstructured speech recognition text is transformed into output structured document text. A probabilistic word substitution model is provided which establishes association probabilities indicative of target structured document text correlating with source unstructured speech recognition text. The input sequence of unstructured speech recognition text is looked up in the word substitution model to determine likelihoods of the represented structured document text corresponding to the text in the input sequence. Then, a most likely sequence of structured document text is generated as an output.

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Och, Franz Joseph, et al, “Discriminative Training and Maximum Entropy Models for Statistical Machine Translation”,Proceedings of the 40thAnnual Meeting of the Association for Computational Linguistics(ACL), Philadelphia, Jul. 2002, pp. 295-302.

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