Darcey Riley

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This paper investigates semi-supervised methods for discriminative language modeling, whereby n-best lists are " hallucinated " for given reference text and are then used for training n-gram language models using the perceptron algorithm. We perform controlled experiments on a very strong baseline English CTS system, comparing three methods for simulating(More)
We present our work on semi-supervised learning of discrim-inative models where the negative examples for sentences in a text corpus are generated using confusion models for Turkish at various granularities, specifically, word, sub-word, syllable and phone levels. We experiment with different language models and various sampling strategies to select(More)
Discriminative language modeling is a structured classification problem. Log-linear models have been previously used to address this problem. In this paper, the standard dot-product feature representation used in log-linear models is replaced by a non-linear function parameterized by a neural network. Embeddings are learned for each word and features are(More)
The perceptron algorithm was used in [1] to estimate discrim-inative language models which correct errors in the output of ASR systems. In its simplest version, the algorithm simply increases the weight of n-gram features which appear in the correct (oracle) hypothesis and decreases the weight of n-gram features which appear in the 1-best hypothesis. In(More)
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