Viseme

About: Viseme is a research topic. Over the lifetime, 865 publications have been published within this topic receiving 17889 citations.

Two articulation models for audiovisual speech synthesis - description and determination.

Abstract: The authors present two visual articulation models for speech synthesis and methods to obtain them from measured data. The visual articulation models are used to control visible articulator movements described by six motion parameters: one for the up-down movement of the lower jaw, three for the lips and two for the tongue (see section 2.1 for details). To obtain the data, a female speaker was measured with the 2Darticulograph AG100 [1] and simultaneously filmed. The first visual articulation model is a hybrid data and rule based model that selects and combines most similar viseme patterns (section 2.3.). It is retrieved more or less directly from the measurements. The second model (section 2.4.) is rule based, following the dominance principal suggested by Lofqvist [2][3]. The parameter values for the second model are derived from the first one. Both models are integrated into MASSY, the Modular Audiovisual Speech SYnthesizer [4].

Kernel-based Persian viseme clustering

Abstract: Viseme (Visual Phoneme) clustering and analysis in every language is among the most important preliminaries for conducting various multimedia researches as talking head, lip reading, lip synchronization and computer assisted pronunciation training applications. With respect to the fact that clustering and analyzing visemes are language dependent processes, we concentrated our research on Persian language, which indeed has suffered from lack of such study. In this paper, we used a hierarchical approach for clustering visemes in Persian language based on principal component analysis of a polynomial kernel matrix considering coarticulation effect. Having obtained feature vector of each phoneme, we applied unweighted pair group method with arithmetic mean to each projected viseme on constructed manifold. Then furthest neighbor of the weight value as a result of reconstruction is set as the criterion for comparing viseme dissimilarity. In order to indicate the robustness of the proposed algorithm, a set of experiments was conducted on Persian databases in which two syllables were examined. Comparing the results of the clustering algorithm with that of the perceptual test given by an expert proves a reasonable evaluation of the proposed algorithm.

ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials

Abstract: Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool’s functionalities.