An elitist approach for extracting automatically well-realized speech sounds with high confidence

This paper presents an "elitist approach" for extracting automatically well-realized speech sounds with high confidence. The elitist approach uses a speech recognition system based on Hidden Markov Models (HMM). The HMM are trained on speech sounds which are systematically well-detected in an iterative procedure. The results show that, by using the HMM models defined in the training phase, the speech recognizer detects reliably specific speech sounds with a small rate of errors.

Using phonetic constraints in acoustic-to-articulatory inversion

The goal of this work is to recover articulatory information from the speech signal by acoustic-to-articulatory inversion. One of the main difficulties with inversion is that the problem is underdetermined and inversion methods generally offer no guarantee on the phonetical realism of the inverse solutions. A way to adress this issue is to use additional phonetic constraints. Knowledge of the phonetic caracteristics of French vowels enable the derivation of reasonable articulatory domains in the space of Maeda parameters: given the formants frequencies (F1,F2,F3) of a speech sample, and thus the vowel identity, an "ideal" articulatory domain can be derived. The space of formants frequencies is partitioned into vowels, using either speaker-specific data or generic information on formants. Then, to each articulatory vector can be associated a phonetic score varying with the distance to the "ideal domain" associated with the corresponding vowel. Inversion experiments were conducted on isolated vowels and vowel-to-vowel transitions. Articulatory parameters were compared with those obtained without using these constraints and those measured from X-ray data.