Richard Beaufort

Richard Beaufort
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Main area of research
Natural language & AI

Richard received his PhD in Computer Science from the University of Namur (Belgium). Prior to studying Computer Science, he graduated with a MS in Language Engineering from the University of Marne-la-Vallée (France), and received both a MS in Computational Linguistics and a BS in Romance philology from the University of Louvain-la-Neuve (Belgium). Before joining Nuance, Richard worked on various NLP-related tasks including text-to-speech synthesis, spelling correction and text similarity measurements, always exploiting the strength of (weighted) finite-state machines. At Nuance, his work is focused on dialog management and natural language generation.

Selected articles

A hybrid rule/model-based finite-state framework for normalizing SMS messages

In recent years, research in natural language processing has increasingly focused on normalizing SMS messages. Different well-defined approaches have been proposed, but the problem remains

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A weighted finite-state framework for correcting errors in natural scene OCR

With the increasing market of cheap cameras, natural scene text has to be handled in an efficient way. Some works deal with text detection in

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Automation of dictation exercises. A working combination of CALL and NLP

This article is in the context of the Computer-Assisted Language Learning (CALL) framework, and addresses more specifically the automation of dictation exercises. It presents a

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Linguistic features weighting for a text-to-speech system without prosody model

This paper presents a Non-Uniform Units selection-based Text- To-Speech synthesizer. Nowadays, systems use prosodic mod- els that do not allow the prosody to vary as

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Comparing ASR modeling methods for spoken dialogue simulation and optimal strategy learning

Speech enabled interfaces are nowadays becoming ubiquitous. The most advanced ones rely on probabilistic pattern matching systems and especially on automatic speech recognition systems. Because

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