Paul van Mulbregt

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Main area of research
Speech recognition

Paul van Mulbregt is Senior Director of Research and Development in the Mobility Division. He has over 20 years of technical leadership and management experience developing technology and then optimizing for products. His technical interests span ASR, NLU and Data Mining. Prior to Nuance, Paul was Assistant Professor of Mathematics at Wellesley College, then joined Dragon Systems and was on the original team developing the MREC speech recognition engine for Dragon Naturally Speaking, the first large vocabulary continuous speech recognition (LVCSR) product for PCs. He then moved to the Voice Signal Technologies startup to develop the E.L.V.I.S. speech recognition and synthesis engine, for command-and-control (VSuite) and the first LVCSR dictation (VoiceMode) application on 100s of millions of mobile phones. Following the acquisition of Voice Signal by Nuance in 2007, Paul led the team developing the core technology for the first hybrid embedded-and-server speech recognition products on mobile phones. Recently he has been thinking about embedded speech recognition again, in particular, systems that are always listening. Paul holds a B.Sc(Hons) from the University of Otago in New Zealand, and a Ph.D. in Mathematics from the Massachusetts Institute of Technology.

Selected articles

Improvements in switchboard recognition and topic identification

We revisit a topic identification test on the Switchboard Corpus first reported by Gillick et al. (see Proc. ICASSP-93, 1993 and ARPA Workshop on Human

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Statistical Models of Topical Content

In this chapter we explore the behavior of two different statistical models, one based on simple unigrams and another based on the beta-binomial distribution, as

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Progress in Recognizing Conversational Telephone Speech

This paper describes improvements made to Dragon’s speech recognition system which have improved performance on Switchboard recognition by roughly 10 percentage points in the past

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A Hidden Markov Model Approach to Text Segmentation and Event Tracking

Continuing progress in the automatic transcription of broadcast speech via speech recognition has raised the possibility of applying information retrieval techniques to the resulting (errorful)

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Text Segmentation and Topic Tracking on Broadcast News via a Hidden Markov Model Approach

Continuing progress in the automatic transcription of broadcast speech via speech recognition has raised the possibility of applying information retrieval techniques to the resulting (errorful)

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