121 research outputs found

    Final Report to NSF of the Standards for Facial Animation Workshop

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    The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

    Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources

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    [ES] En los últimos años, el aprendizaje profundo ha cambiado significativamente el panorama en diversas áreas del campo de la inteligencia artificial, entre las que se incluyen la visión por computador, el procesamiento del lenguaje natural, robótica o teoría de juegos. En particular, el sorprendente éxito del aprendizaje profundo en múltiples aplicaciones del campo del procesamiento del lenguaje natural tales como el reconocimiento automático del habla (ASR), la traducción automática (MT) o la síntesis de voz (TTS), ha supuesto una mejora drástica en la precisión de estos sistemas, extendiendo así su implantación a un mayor rango de aplicaciones en la vida real. En este momento, es evidente que las tecnologías de reconocimiento automático del habla y traducción automática pueden ser empleadas para producir, de forma efectiva, subtítulos multilingües de alta calidad de contenidos audiovisuales. Esto es particularmente cierto en el contexto de los vídeos educativos, donde las condiciones acústicas son normalmente favorables para los sistemas de ASR y el discurso está gramaticalmente bien formado. Sin embargo, en el caso de TTS, aunque los sistemas basados en redes neuronales han demostrado ser capaces de sintetizar voz de un realismo y calidad sin precedentes, todavía debe comprobarse si esta tecnología está lo suficientemente madura como para mejorar la accesibilidad y la participación en el aprendizaje en línea. Además, existen diversas tareas en el campo de la síntesis de voz que todavía suponen un reto, como la clonación de voz inter-lingüe, la síntesis incremental o la adaptación zero-shot a nuevos locutores. Esta tesis aborda la mejora de las prestaciones de los sistemas actuales de síntesis de voz basados en redes neuronales, así como la extensión de su aplicación en diversos escenarios, en el contexto de mejorar la accesibilidad en el aprendizaje en línea. En este sentido, este trabajo presta especial atención a la adaptación a nuevos locutores y a la clonación de voz inter-lingüe, ya que los textos a sintetizar se corresponden, en este caso, a traducciones de intervenciones originalmente en otro idioma.[CA] Durant aquests darrers anys, l'aprenentatge profund ha canviat significativament el panorama en diverses àrees del camp de la intel·ligència artificial, entre les quals s'inclouen la visió per computador, el processament del llenguatge natural, robòtica o la teoria de jocs. En particular, el sorprenent èxit de l'aprenentatge profund en múltiples aplicacions del camp del processament del llenguatge natural, com ara el reconeixement automàtic de la parla (ASR), la traducció automàtica (MT) o la síntesi de veu (TTS), ha suposat una millora dràstica en la precisió i qualitat d'aquests sistemes, estenent així la seva implantació a un ventall més ampli a la vida real. En aquest moment, és evident que les tecnologies de reconeixement automàtic de la parla i traducció automàtica poden ser emprades per a produir, de forma efectiva, subtítols multilingües d'alta qualitat de continguts audiovisuals. Això és particularment cert en el context dels vídeos educatius, on les condicions acústiques són normalment favorables per als sistemes d'ASR i el discurs està gramaticalment ben format. No obstant això, al cas de TTS, encara que els sistemes basats en xarxes neuronals han demostrat ser capaços de sintetitzar veu d'un realisme i qualitat sense precedents, encara s'ha de comprovar si aquesta tecnologia és ja prou madura com per millorar l'accessibilitat i la participació en l'aprenentatge en línia. A més, hi ha diverses tasques al camp de la síntesi de veu que encara suposen un repte, com ara la clonació de veu inter-lingüe, la síntesi incremental o l'adaptació zero-shot a nous locutors. Aquesta tesi aborda la millora de les prestacions dels sistemes actuals de síntesi de veu basats en xarxes neuronals, així com l'extensió de la seva aplicació en diversos escenaris, en el context de millorar l'accessibilitat en l'aprenentatge en línia. En aquest sentit, aquest treball presta especial atenció a l'adaptació a nous locutors i a la clonació de veu interlingüe, ja que els textos a sintetitzar es corresponen, en aquest cas, a traduccions d'intervencions originalment en un altre idioma.[EN] In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including computer vision, natural language processing, robotics, and game theory. In particular, the striking success of deep learning in a large variety of natural language processing (NLP) applications, including automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS), has resulted in major accuracy improvements, thus widening the applicability of these technologies in real-life settings. At this point, it is clear that ASR and MT technologies can be utilized to produce cost-effective, high-quality multilingual subtitles of video contents of different kinds. This is particularly true in the case of transcription and translation of video lectures and other kinds of educational materials, in which the audio recording conditions are usually favorable for the ASR task, and there is a grammatically well-formed speech. However, although state-of-the-art neural approaches to TTS have shown to drastically improve the naturalness and quality of synthetic speech over conventional concatenative and parametric systems, it is still unclear whether this technology is already mature enough to improve accessibility and engagement in online learning, and particularly in the context of higher education. Furthermore, advanced topics in TTS such as cross-lingual voice cloning, incremental TTS or zero-shot speaker adaptation remain an open challenge in the field. This thesis is about enhancing the performance and widening the applicability of modern neural TTS technologies in real-life settings, both in offline and streaming conditions, in the context of improving accessibility and engagement in online learning. Thus, particular emphasis is placed on speaker adaptation and cross-lingual voice cloning, as the input text corresponds to a translated utterance in this context.Pérez González De Martos, AM. (2022). Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/184019TESISPremios Extraordinarios de tesis doctorale

    Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

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    The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal speech data can be collected, the risk of malicious use of voice-cloning and speaker/gender/pathological/etc. recognition has increased. This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization. In this work, anonymization refers to making personal speech data unlinkable to an identity while maintaining the usefulness (utility) of the speech signal (e.g., access to linguistic content). We start by identifying several challenges that evaluation protocols need to consider to evaluate the degree of privacy protection properly. We clarify how anonymization systems must be configured for evaluation purposes and highlight that many practical deployment configurations do not permit privacy evaluation. Furthermore, we study and examine the most common voice conversion-based anonymization system and identify its weak points before suggesting new methods to overcome some limitations. We isolate all components of the anonymization system to evaluate the degree of speaker PPI associated with each of them. Then, we propose several transformation methods for each component to reduce as much as possible speaker PPI while maintaining utility. We promote anonymization algorithms based on quantization-based transformation as an alternative to the most-used and well-known noise-based approach. Finally, we endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy | for associated source code, see https://github.com/deep-privacy/SA-toolki

    A Parametric Sound Object Model for Sound Texture Synthesis

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    This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

    Audio Virology and Affect Contagion in the Times of Preemptive Power and Sonic Futurism: The Sonic Warfare of Fatima Al Qadiri

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    This project examines the State’s use of sound technologies in particular to conjure affects facilitative of the maintenance and control of human bodies and political activities. In tension with this current, it will also study the subversion of sonic war machinery by cultural workers and musicians in the production of transnational political solidarities against the state militarization/securitization of life and preemption/commodification of death–a socio-economic paradigm fed by the (neo)colonial underbellies of capitalist modernity, from the Transatlantic Slave Trade to the colonization and military exploitation of the ‘Middle East’

    Application-driven visual computing towards industry 4.0 2018

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    245 p.La Tesis recoge contribuciones en tres campos: 1. Agentes Virtuales Interactivos: autónomos, modulares, escalables, ubicuos y atractivos para el usuario. Estos IVA pueden interactuar con los usuarios de manera natural.2. Entornos de RV/RA Inmersivos: RV en la planificación de la producción, el diseño de producto, la simulación de procesos, pruebas y verificación. El Operario Virtual muestra cómo la RV y los Co-bots pueden trabajar en un entorno seguro. En el Operario Aumentado la RA muestra información relevante al trabajador de una manera no intrusiva. 3. Gestión Interactiva de Modelos 3D: gestión online y visualización de modelos CAD multimedia, mediante conversión automática de modelos CAD a la Web. La tecnología Web3D permite la visualización e interacción de estos modelos en dispositivos móviles de baja potencia.Además, estas contribuciones han permitido analizar los desafíos presentados por Industry 4.0. La tesis ha contribuido a proporcionar una prueba de concepto para algunos de esos desafíos: en factores humanos, simulación, visualización e integración de modelos

    Communicative humanoids : a computational model of psychosocial dialogue skills

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (p. [223]-238).Kristinn Rúnar Thórisson.Ph.D
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