Paul Vozila

Paul Vozila
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Main area of research
Speech recognition

Paul Vozila joined Nuance (nee Scansoft) in 2001. He obtained a Masters in Mathematics from Northeastern University and a Bachelors in Computer Science and Mathematics from Johns Hopkins University. His main research interests include automatic speech recognition, language modeling and natural language understanding.

Selected articles

Semi-supervised Chinese word segmentation using partial-label learning with conditional random fields

There is rich knowledge encoded in online web data. For example, punctuation and entity tags in Wikipedia data define some word boundaries in a sentence.

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Joint training of interpolated exponential n-gram models

For many speech recognition tasks, the best language model performance is achieved by collecting text from multiple sources or domains, and interpolating language models built

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An empirical study of semi-supervised chinese word segmentation Using co-training

In this paper we report an empirical study on semi-supervised Chinese word segmentation using co-training. We utilize two segmenters:1) a word-based segmenter leveraginga word-level language

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Improved models for automatic punctuation prediction for spoken and written text

This paper presents improved models for the automatic prediction of punctuation marks in written or spoken text.Various kinds of textual features are combined using Conditional

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Large scale hierarchical neural network language models

Feed-forward neural network language models (NNLMs) are known to improve both perplexity and word error rate performance for speech recognition compared with conventional ngram language

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