A phonetic-contrast motivated adaptation to control the degree-of-articulation on Italian HMM-based synthetic voices

The effectiveness of phonetic-contrast motivated adaptation on HMM-based synthetic voices was previously tested on English successfully. The aim of this paper is to prove that such adaptation can be exported with minor changes to languages having different intrinsic characteristics. The Italian language was chosen because it has no obvious phonemic configuration towards which human speech tend when hypo-articulated such as the mid-central vowel (schwa) for English. Nonetheless, low-contrastive attractors were identified and a linear transformation was trained by contrasting each phone pronunciation with its nearest acoustic neighbour. Different degree of hyper and hypo articulated synthetic speech was then achieved by scaling such adaptation along the dimension identified by each contrastive pair. The Italian synthesiser outcome adapted with both the maximum and the minimum transformation strength was evaluated with two objective assessments: the analysis of some common acoustic correlates and the measurement of a intelligibility-in-noise index. For the latter, signals were mixed with different disturbances at various energy ratios and intelligibility was compared to the standard-TTS generated speech. The experimental results proved such transformation on the Italian voices to be as effective as those on the English one.