Philip Clarkson

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The CMU Statistical Language Modeling toolkit was released in 1994 in order to facilitate the construction and testing of bigram and trigram language models. It is currently in use in over 40 academic, government and industrial laboratories in over 12 countries. This paper presents a new version of the toolkit. We outline the conventional language modeling(More)
This paper presents two techniques for language model adaptation. The rst is based on the use of mixtures of language models: the training text is partitioned according to topic, a language model is constructed for each component, and at recognition time appropriate weightings are assigned to each component to model the observed style of language. The(More)
Adaptive language models have consistently been shown to lead to a significant reduction in language model perplexity compared to the equivalent static trigram model on many data sets. When these language models have been applied to speech recognition, however, they have seldom resulted in a corresponding reduction in word error rate. This paper will(More)
In this paper, we derive an algorithm to train support vector machines sequentially. The algorithm makes use of the Kalman lter and is optimal in a minimum variance framework. It extends the support vector machine paradigm to applications involving real-time and non-stationary signal processing. It also provides a computationally eecient alternative to the(More)
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