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Equipe de recherche PAROLE

Rapports d'activité

Overall Objectives

PAROLE is a joint project to Inria, CNRS, University of Lorraine through the LORIA laboratory (UMR 7503). The purpose of our project is to automatically process speech signals to understand their meaning, and to analyze and enhance their acoustic structure. It inscribes within the view of offering efficient vocal technologies and necessitates works in analysis, perception and automatic recognition (ASR) of speech.

Our activities are structured in three topics:

Speech analysis and synthesis. Our works are concerned with automatic extraction and perception of acoustic and visual cues, acoustic-to-articulatory inversion and speech synthesis. These themes give rise to a number of ongoing and future applications especially in the domain of foreign language learning.

Enriched automatic speech recognition. Our works are concerned with stochastic models (HMM Hidden Markov Models and Bayesian networks), semi-supervised and smoothed training of these stochastic models, adaptation of a recognition system to important variability sources, and with enriching the output of speech recognition with higher-level information such as syntactic structure and punctuation marks. These topics give also rise to a number of ongoing and future applications: automatic transcription, speech/text alignment, audio indexing, keyword spotting, foreign language learning, dialog systems, vocal services...

Speech to speech translation and language modeling. This axis concerns statistical machine translation. The objective is to translate speech from a source language to any target language. The main activity of the group which is in charge of this axis is to propose an alternative method to the classical five IBM's models. This activity should conduct to several applications: e-mail speech to text, translation of movie subtitles.

Our pluridisciplinary scientific culture combines works in phonetics, pattern recognition and artificial intelligence. This pluridisciplinarity turns out to be a decisive asset to address new research topics, particularly language learning that simultaneously require competences in automatic speech recognition and phonetics.

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