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- W2109622018 abstract "This paper investigates discriminative language modeling in a scenario with two kinds of observed errors: errors in ASR transcription and errors in utterance classification. Using the perceptron algorithm, we train joint language and class models either independently or simultaneously, under various parameter update conditions. On a large vocabulary customer service call-classification application, we show that simultaneous optimization of class, n-gram, and class/n-gram feature weights results in a significant WER reduction over a model using just n-gram features, while additionally significantly outperforming a deployed baseline in classification error rate. A range of parameter update approaches for the various feature sets are presented and evaluated. The resulting models are encoded as weighted finite-state automata, and are used by intersecting the model with word lattices." @default.
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- W2109622018 date "2006-10-11" @default.
- W2109622018 modified "2023-09-28" @default.
- W2109622018 title "Joint Discriminative Language Modeling and Utterance Classification" @default.
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- W2109622018 doi "https://doi.org/10.1109/icassp.2005.1415175" @default.
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