Accurate 3D mouth localization from a single camera remains challenging in smart spaces, where speakers appear at low resolution and under unconstrained head poses. Most existing audiovisual tracking methods approximate the mouth position using a fixed location within the detected face bounding box, implicitly assuming frontal pose and leading to large errors under head rotations. In addition, monocular 3D estimation suffers from scale ambiguity, i.e., the inherent uncertainty between object size and distance in 2D image projections. We investigate whether head pose estimation (HPE) can overcome these limitations. Two monocular approaches are proposed: LPoseVL , combining facial landmarks with geometric pose recovery using a rigid 3D head model, and LFreeVL , based on landmark-free 6DoF pose estimation with calibration. Experiments on the AV16.3 and CAV3D datasets show that explicitly modeling head pose significantly improves accuracy, achieving up to 46.9% error reduction over bounding-box-based state-of-the-art methods.

This work presents a novel proposal aimed at automating the assessment of human functional limitations. It represents an interdisciplinary effort to develop and clinically evaluate an automated system for objective functional assessment, and is being carried out within the EYEFUL research project. Functional evaluation is a complex task that requires a holistic approach, considering various components like cognition, motor skills, and executive functions. The EYEFUL project has created an intelligent space within a fully equipped apartment, which includes the deployment of advanced technology (cameras, microphones, and wearable devices), to capture data from subjects undergoing assessment. In this paper, we provide an extensive overview of the project's architecture, including the selection of Activities of Daily Living (ADLs), the recruitment of subjects, the validation methodology, and the experimental setup. We describe the specialized apartment and sensor hardware deployed for data collection, along with the initial machine learning systems for data analysis. Additionally, we analyze the existing state-of-the-art research in automatic functional performance evaluation and present our proposal. This encompasses ADL selection, database design, recording considerations, and an overview of technological modules to be implemented. We also discuss the current status of select modules, including quantitative and qualitative results and references to ongoing work. In conclusion, the EYEFUL project is pioneering automated functional assessment, integrating advanced technology and interdisciplinary collaboration. It aims to provide healthcare professionals with accurate, comprehensive, and clinically validated evaluations of human functionality during ADLs, addressing the complex nature of functional limitations.

This paper proposes an exponential interpolation to merge a part-of-speech-based language model and a word-based$n$-gram language model to accomplish word prediction tasks. In order to find a set of mathematical equations to properly describe the language modeling, a model based on partial differential equations is proposed. With the appropriate initial conditions, it was found an interpolation model similar to the traditional maximum entropy language model. Improvements in keystroke saved and perplexity over the word-based$n$-gram language model and two other traditional interpolation models is obtained, considering three different languages. The proposed interpolation model also provides additional improvement in hit rate parameter.

This paper presents an algorithm that automatically corrects the distortion caused by multipath interference (MpI) in depth measurements obtained with time of flight cameras (ToF cameras). A radiometric model that explains, under some mild simplifications, the working principle of a ToF camera including a model for MpI is proposed. Using this model we demonstrate that all the information needed for compensating the influence of MpI on the scene captured by the camera is self-contained in the measurements (depth and amplitude of infrared signal). We propose an iterative optimization method that, based on the measurements contaminated with MpI, gives depth correction for each pixel. Results are shown in artificially generated time of flight scenes using the radiometric model. In addition, the system has been validated in real scenes using a commercial ToF camera providing good results.

In this work, the use of the Generalized Principal Components Analysis (G-PCA) to improve the segmentation of moving objects in image sequences is proposed. In order to obtain this improvement, the noise components in the image derivatives are reduced, and afterwards, a method based on linear algebra is used to make the segmentation. Furthermore this work presents diverse tests to compare the results reached with and without the noise reduction in the image derivatives, and using the nonlinear minimization of an error function. A remarkable improvement in the segmentation quality and the processing time can be observed in every experiment when using the proposed method.

Modern Speech synthesisers have achieved a high degree of intelligibility, but can not be regarded as natural-sounding devices. In order to decrease the monotony of synthetic speech, the implementation of emotional effects is now being progressively considered. This paper presents a through study of emotional speech in Spanish, and its application to TTS, presenting a prototype system that simulates emotional speech using a commercial synthesiser. The design and recording of a Spanish database will be described and also the analysis of the emotional prosody (by fitting the data to a formal model). Using this collected data, a rule-based simulation of three primary emotions was implemented in the Text-to-Speech system. Finally, the assessment of the synthetic voice through perception experiments will classify the system as capable of producing quality voice with recognisable emotional effects.