Expressive Modulation of Neutral Visual Speech

The need for animated graphical models of the human face is commonplace in the movies, video games and television industries, appearing in everything from low budget advertisements and free mobile apps, to Hollywood blockbusters costing hundreds of millions of dollars. Generative statistical models of animation attempt to address some of the drawbacks of industry standard practices such as labour intensity and creative inflexibility. This work describes one such method for transforming speech animation curves between different expressive styles. Beginning with the assumption that expressive speech animation is a mix of two components, a high-frequency speech component (the content) and a much lower-frequency expressive component (the style), we use Independent Component Analysis (ICA) to identify and manipulate these components independently of one another. Next we learn how the energy for different speaking styles is distributed in terms of the low-dimensional independent components model. Transforming the speaking style involves projecting new animation curves into the lowdimensional ICA space, redistributing the energy in the independent components, and finally reconstructing the animation curves by inverting the projection. We show that a single ICA model can be used for separating multiple expressive styles into their component parts. Subjective evaluations show that viewers can reliably identify the expressive style generated using our approach, and that they have difficulty in identifying transformed animated expressive speech from the equivalent ground-truth

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Proceedings of the 2nd European conference on disability, virtual reality and associated technologies (ECDVRAT 1998)

The proceedings of the conferenc

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Designing Embodied Interactive Software Agents for E-Learning: Principles, Components, and Roles

Embodied interactive software agents are complex autonomous, adaptive, and social software systems with a digital embodiment that enables them to act on and react to other entities (users, objects, and other agents) in their environment through bodily actions, which include the use of verbal and non-verbal communicative behaviors in face-to-face interactions with the user. These agents have been developed for various roles in different application domains, in which they perform tasks that have been assigned to them by their developers or delegated to them by their users or by other agents. In computer-assisted learning, embodied interactive pedagogical software agents have the general task to promote human learning by working with students (and other agents) in computer-based learning environments, among them e-learning platforms based on Internet technologies, such as the Virtual Linguistics Campus (www.linguistics-online.com). In these environments, pedagogical agents provide contextualized, qualified, personalized, and timely assistance, cooperation, instruction, motivation, and services for both individual learners and groups of learners. This thesis develops a comprehensive, multidisciplinary, and user-oriented view of the design of embodied interactive pedagogical software agents, which integrates theoretical and practical insights from various academic and other fields. The research intends to contribute to the scientific understanding of issues, methods, theories, and technologies that are involved in the design, implementation, and evaluation of embodied interactive software agents for different roles in e-learning and other areas. For developers, the thesis provides sixteen basic principles (Added Value, Perceptible Qualities, Balanced Design, Coherence, Consistency, Completeness, Comprehensibility, Individuality, Variability, Communicative Ability, Modularity, Teamwork, Participatory Design, Role Awareness, Cultural Awareness, and Relationship Building) plus a large number of specific guidelines for the design of embodied interactive software agents and their components. Furthermore, it offers critical reviews of theories, concepts, approaches, and technologies from different areas and disciplines that are relevant to agent design. Finally, it discusses three pedagogical agent roles (virtual native speaker, coach, and peer) in the scenario of the linguistic fieldwork classes on the Virtual Linguistics Campus and presents detailed considerations for the design of an agent for one of these roles (the virtual native speaker)

Towards spatial and temporal analysis of facial expressions in 3D data

Facial expressions are one of the most important means for communication of emotions and meaning. They are used to clarify and give emphasis, to express intentions, and form a crucial part of any human interaction. The ability to automatically recognise and analyse expressions could therefore prove to be vital in human behaviour understanding, which has applications in a number of areas such as psychology, medicine and security. 3D and 4D (3D+time) facial expression analysis is an expanding field, providing the ability to deal with problems inherent to 2D images, such as out-of-plane motion, head pose, and lighting and illumination issues. Analysis of data of this kind requires extending successful approaches applied to the 2D problem, as well as the development of new techniques. The introduction of recent new databases containing appropriate expression data, recorded in 3D or 4D, has allowed research into this exciting area for the first time. This thesis develops a number of techniques, both in 2D and 3D, that build towards a complete system for analysis of 4D expressions. Suitable feature types, designed by employing binary pattern methods, are developed for analysis of 3D facial geometry data. The full dynamics of 4D expressions are modelled, through a system reliant on motion-based features, to demonstrate how the different components of the expression (neutral-onset-apex-offset) can be distinguished and harnessed. Further, the spatial structure of expressions is harnessed to improve expression component intensity estimation in 2D videos. Finally, it is discussed how this latter step could be extended to 3D facial expression analysis, and also combined with temporal analysis. Thus, it is demonstrated that both spatial and temporal information, when combined with appropriate 3D features, is critical in analysis of 4D expression data.Open Acces