Structuration des informations acoustiques dans le projet A.R.I.A.L.

Abstract

The A.R.I.A.L. (Analyse et Reconnaissance des Informations Acoustiques et Linguistiques = Analysis and recognition of acoustic and linguistic data.) project uses acoustic and phonetic data coming from an ear model and structured in an ascending manner. The data are sometimes organized as features and sometimes as patterns and take into account context effects coded in the same way as instantaneous parameters. Our discussion will concern neither the features nor the pattern concepts, nor indeed their efficiency for recognition. What we shall do is to describe the A.R.I.A.L. project data basis. 1. 1. The acoustic data are organized hierarchically: on level 1: spectral data, intensity and pitch, on level 2: acoustic cues, on level 3: discrete cues (pseudo-features). 2. 2. Segmentation produces short, homogeneous segments (infra-phonemes) in whose all the cues take part in the implementation. A phonetic unit (phoneme, syllable, etc.) is described as a series of segments labelled S i . 3. 3. The knowledge basis is built up automatically without the help of an expert except for the initial manual segmentation of the sentences analysed. All the segments encountered make up an alphabet ( S i ). 3.1. Reduction of the alphabet by means of a metric routine. 3.2. Setting up of the base: U k / C l S a S b —, Unit U k in context C l is a series of ( S i ) segments. It is then possible to define the classes of equivalence between series of segments, and this makes it possible (a) to give a single rewrite rule for U k / C l , (b) to study the oppositions between units. 3.3. Reduction of the data basis using a metric routine and dynamic programming.