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Section: Scientific Foundations


Research in speech processing gave rise to two kinds of approaches:

The former research topic is motivated by the high specificity of speech among other acoustical signals: the speech production system is easily accessible and measurable (at least at first approach); acoustical equations are reasonably difficult from a mathematical point of view (with simplifications that are moderately restrictive); sentences built by speakers are governed by vocabulary and grammar of the considered language. This led acousticians to develop research aiming at generating artificial speech signals of good quality, and phoneticians to develop research aiming at finding out the origin of speech sound variability and at explaining how articulators are utilized, how sounds of a language are structured and how they influence each other in continuous speech. Lastly, that led linguists to study how sentences are built. Clearly, this approach gives rise to a number of exchanges between theory and experimentation and it turns out that all these aspects of speech cannot be mastered easily at the same time.

Results available on speech production and perception do not enable using an analysis by synthesis approach for automatic speech recognition. Automatic speech recognition thus gives rise to a second approach that consists in modeling observations of speech production and perception. Efforts focused onto the design of numerical models (first simple vectors of spectral shapes and now stochastic or neural models) of word or phoneme acoustical realizations, and onto the development of statistical language models.

These two approaches are complementary; the latter borrows theoretical results on speech from the former, which, in its turn, borrows some numerical methods. Spectral analysis methods are undoubtedly the domain where exchanges are most marked. The simultaneous existence of these two approaches is one of the particularities of speech research conducted in Nancy and we intend to enhance exchanges between them. These exchanges will probably grow in number because of new applications like: (i) computer aided foreign language learning which requires both reliable automatic speech recognition and fine acoustic and articulatory speech analysis, (ii) automatic recognition of spontaneous speech which requires robustness against noise and speaker variability.


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