Auf einem menschlichen Gehörmodell basierende Elektrodenstimulationsstrategie für Cochleaimplantate

Cochleaimplantate (CI), verbunden mit einer professionellen Rehabilitation, haben mehreren hunderttausenden Hörgeschädigten die verbale Kommunikation wieder ermöglicht. Betrachtet man jedoch die Rehabilitationserfolge, so haben CI-Systeme inzwischen ihre Grenzen erreicht. Die Tatsache, dass die meisten CI-Träger nicht in der Lage sind, Musik zu genießen oder einer Konversation in geräuschvoller Umgebung zu folgen, zeigt, dass es noch Raum für Verbesserungen gibt.Diese Dissertation stellt die neue CI-Signalverarbeitungsstrategie Stimulation based on Auditory Modeling (SAM) vor, die vollständig auf einem Computermodell des menschlichen peripheren Hörsystems beruht.Im Rahmen der vorliegenden Arbeit wurde die SAM Strategie dreifach evaluiert: mit vereinfachten Wahrnehmungsmodellen von CI-Nutzern, mit fünf CI-Nutzern, und mit 27 Normalhörenden mittels eines akustischen Modells der CI-Wahrnehmung. Die Evaluationsergebnisse wurden stets mit Ergebnissen, die durch die Verwendung der Advanced Combination Encoder (ACE) Strategie ermittelt wurden, verglichen. ACE stellt die zurzeit verbreitetste Strategie dar. Erste Simulationen zeigten, dass die Sprachverständlichkeit mit SAM genauso gut wie mit ACE ist. Weiterhin lieferte SAM genauere binaurale Merkmale, was potentiell zu einer Verbesserung der Schallquellenlokalisierungfähigkeit führen kann. Die Simulationen zeigten ebenfalls einen erhöhten Anteil an zeitlichen Pitchinformationen, welche von SAM bereitgestellt wurden. Die Ergebnisse der nachfolgenden Pilotstudie mit fünf CI-Nutzern zeigten mehrere Vorteile von SAM auf. Erstens war eine signifikante Verbesserung der Tonhöhenunterscheidung bei Sinustönen und gesungenen Vokalen zu erkennen. Zweitens bestätigten CI-Nutzer, die kontralateral mit einem Hörgerät versorgt waren, eine natürlicheren Klangeindruck. Als ein sehr bedeutender Vorteil stellte sich drittens heraus, dass sich alle Testpersonen in sehr kurzer Zeit (ca. 10 bis 30 Minuten) an SAM gewöhnen konnten. Dies ist besonders wichtig, da typischerweise Wochen oder Monate nötig sind. Tests mit Normalhörenden lieferten weitere Nachweise für die verbesserte Tonhöhenunterscheidung mit SAM.Obwohl SAM noch keine marktreife Alternative ist, versucht sie den Weg für zukünftige Strategien, die auf Gehörmodellen beruhen, zu ebnen und ist somit ein erfolgversprechender Kandidat für weitere Forschungsarbeiten.Cochlear implants (CIs) combined with professional rehabilitation have enabled several hundreds of thousands of hearing-impaired individuals to re-enter the world of verbal communication. Though very successful, current CI systems seem to have reached their peak potential. The fact that most recipients claim not to enjoy listening to music and are not capable of carrying on a conversation in noisy or reverberative environments shows that there is still room for improvement.This dissertation presents a new cochlear implant signal processing strategy called Stimulation based on Auditory Modeling (SAM), which is completely based on a computational model of the human peripheral auditory system.SAM has been evaluated through simplified models of CI listeners, with five cochlear implant users, and with 27 normal-hearing subjects using an acoustic model of CI perception. Results have always been compared to those acquired using Advanced Combination Encoder (ACE), which is today’s most prevalent CI strategy. First simulations showed that speech intelligibility of CI users fitted with SAM should be just as good as that of CI listeners fitted with ACE. Furthermore, it has been shown that SAM provides more accurate binaural cues, which can potentially enhance the sound source localization ability of bilaterally fitted implantees. Simulations have also revealed an increased amount of temporal pitch information provided by SAM. The subsequent pilot study, which ran smoothly, revealed several benefits of using SAM. First, there was a significant improvement in pitch discrimination of pure tones and sung vowels. Second, CI users fitted with a contralateral hearing aid reported a more natural sound of both speech and music. Third, all subjects were accustomed to SAM in a very short period of time (in the order of 10 to 30 minutes), which is particularly important given that a successful CI strategy change typically takes weeks to months. An additional test with 27 normal-hearing listeners using an acoustic model of CI perception delivered further evidence for improved pitch discrimination ability with SAM as compared to ACE.Although SAM is not yet a market-ready alternative, it strives to pave the way for future strategies based on auditory models and it is a promising candidate for further research and investigation

Joint University Program for Air Transportation Research, 1988-1989

The research conducted during 1988 to 1989 under the NASA/FAA-sponsored Joint University Program for Air Transportation Research is summarized. The Joint University Program is a coordinated set of three grants sponsored by NASA Langley Research Center and the Federal Aviation Administration, one each with the Massachusetts Institute of Technology, Ohio University, and Princeton University. Completed works, status reports, and annotated bibliographies are presented for research topics, which include computer science, guidance and control theory and practice, aircraft performance, flight dynamics, and applied experimental psychology. An overview of the year's activities for each university is also presented

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Human-Robot Interaction architecture for interactive and lively social robots

Mención Internacional en el título de doctorLa sociedad está experimentando un proceso de envejecimiento que puede provocar un desequilibrio entre la población en edad de trabajar y aquella fuera del mercado de trabajo. Una de las soluciones a este problema que se están considerando hoy en día es la introducción de robots en multiples sectores, incluyendo el de servicios. Sin embargo, para que esto sea una solución viable, estos robots necesitan ser capaces de interactuar con personas de manera satisfactoria, entre otras habilidades. En el contexto de la aplicación de robots sociales al cuidado de mayores, esta tesis busca proporcionar a un robot social las habilidades necesarias para crear interacciones entre humanos y robots que sean naturales. En concreto, esta tesis se centra en tres problemas que deben ser solucionados: (i) el modelado de interacciones entre humanos y robots; (ii) equipar a un robot social con las capacidades expresivas necesarias para una comunicación satisfactoria; y (iii) darle al robot una apariencia vivaz. La solución al problema de modelado de diálogos presentada en esta tesis propone diseñar estos diálogos como una secuencia de elementos atómicos llamados Actos Comunicativos (CAs, por sus siglas en inglés). Se pueden parametrizar en tiempo de ejecución para completar diferentes objetivos comunicativos, y están equipados con mecanismos para manejar algunas de las imprecisiones que pueden aparecer durante interacciones. Estos CAs han sido identificados a partir de la combinación de dos dimensiones: iniciativa (si la tiene el robot o el usuario) e intención (si se pretende obtener o proporcionar información). Estos CAs pueden ser combinados siguiendo una estructura jerárquica para crear estructuras mas complejas que sean reutilizables. Esto simplifica el proceso para crear nuevas interacciones, permitiendo a los desarrolladores centrarse exclusivamente en diseñar el flujo del diálogo, sin tener que preocuparse de reimplementar otras funcionalidades que tienen que estar presentes en todas las interacciones (como el manejo de errores, por ejemplo). La expresividad del robot está basada en el uso de una librería de gestos, o expresiones, multimodales predefinidos, modelados como estructuras similares a máquinas de estados. El módulo que controla la expresividad recibe peticiones para realizar dichas expresiones, planifica su ejecución para evitar cualquier conflicto que pueda aparecer, las carga, y comprueba que su ejecución se complete sin problemas. El sistema es capaz también de generar estas expresiones en tiempo de ejecución a partir de una lista de acciones unimodales (como decir una frase, o mover una articulación). Una de las características más importantes de la arquitectura de expresividad propuesta es la integración de una serie de métodos de modulación que pueden ser usados para modificar los gestos del robot en tiempo de ejecución. Esto permite al robot adaptar estas expresiones en base a circunstancias particulares (aumentando al mismo tiempo la variabilidad de la expresividad del robot), y usar un número limitado de gestos para mostrar diferentes estados internos (como el estado emocional). Teniendo en cuenta que ser reconocido como un ser vivo es un requisito para poder participar en interacciones sociales, que un robot social muestre una apariencia de vivacidad es un factor clave en interacciones entre humanos y robots. Para ello, esta tesis propone dos soluciones. El primer método genera acciones a través de las diferentes interfaces del robot a intervalos. La frecuencia e intensidad de estas acciones están definidas en base a una señal que representa el pulso del robot. Dicha señal puede adaptarse al contexto de la interacción o al estado interno del robot. El segundo método enriquece las interacciones verbales entre el robot y el usuario prediciendo los gestos no verbales más apropiados en base al contenido del diálogo y a la intención comunicativa del robot. Un modelo basado en aprendizaje automático recibe la transcripción del mensaje verbal del robot, predice los gestos que deberían acompañarlo, y los sincroniza para que cada gesto empiece en el momento preciso. Este modelo se ha desarrollado usando una combinación de un encoder diseñado con una red neuronal Long-Short Term Memory, y un Conditional Random Field para predecir la secuencia de gestos que deben acompañar a la frase del robot. Todos los elementos presentados conforman el núcleo de una arquitectura de interacción humano-robot modular que ha sido integrada en múltiples plataformas, y probada bajo diferentes condiciones. El objetivo central de esta tesis es contribuir al área de interacción humano-robot con una nueva solución que es modular e independiente de la plataforma robótica, y que se centra en proporcionar a los desarrolladores las herramientas necesarias para desarrollar aplicaciones que requieran interacciones con personas.Society is experiencing a series of demographic changes that can result in an unbalance between the active working and non-working age populations. One of the solutions considered to mitigate this problem is the inclusion of robots in multiple sectors, including the service sector. But for this to be a viable solution, among other features, robots need to be able to interact with humans successfully. This thesis seeks to endow a social robot with the abilities required for a natural human-robot interactions. The main objective is to contribute to the body of knowledge on the area of Human-Robot Interaction with a new, platform-independent, modular approach that focuses on giving roboticists the tools required to develop applications that involve interactions with humans. In particular, this thesis focuses on three problems that need to be addressed: (i) modelling interactions between a robot and an user; (ii) endow the robot with the expressive capabilities required for a successful communication; and (iii) endow the robot with a lively appearance. The approach to dialogue modelling presented in this thesis proposes to model dialogues as a sequence of atomic interaction units, called Communicative Acts, or CAs. They can be parametrized in runtime to achieve different communicative goals, and are endowed with mechanisms oriented to solve some of the uncertainties related to interaction. Two dimensions have been used to identify the required CAs: initiative (the robot or the user), and intention (either retrieve information or to convey it). These basic CAs can be combined in a hierarchical manner to create more re-usable complex structures. This approach simplifies the creation of new interactions, by allowing developers to focus exclusively on designing the flow of the dialogue, without having to re-implement functionalities that are common to all dialogues (like error handling, for example). The expressiveness of the robot is based on the use of a library of predefined multimodal gestures, or expressions, modelled as state machines. The module managing the expressiveness receives requests for performing gestures, schedules their execution in order to avoid any possible conflict that might arise, loads them, and ensures that their execution goes without problems. The proposed approach is also able to generate expressions in runtime based on a list of unimodal actions (an utterance, the motion of a limb, etc...). One of the key features of the proposed expressiveness management approach is the integration of a series of modulation techniques that can be used to modify the robot’s expressions in runtime. This would allow the robot to adapt them to the particularities of a given situation (which would also increase the variability of the robot expressiveness), and to display different internal states with the same expressions. Considering that being recognized as a living being is a requirement for engaging in social encounters, the perception of a social robot as a living entity is a key requirement to foster human-robot interactions. In this dissertation, two approaches have been proposed. The first method generates actions for the different interfaces of the robot at certain intervals. The frequency and intensity of these actions are defined by a signal that represents the pulse of the robot, which can be adapted to the context of the interaction or the internal state of the robot. The second method enhances the robot’s utterance by predicting the appropriate non-verbal expressions that should accompany them, according to the content of the robot’s message, as well as its communicative intention. A deep learning model receives the transcription of the robot’s utterances, predicts which expressions should accompany it, and synchronizes them, so each gesture selected starts at the appropriate time. The model has been developed using a combination of a Long-Short Term Memory network-based encoder and a Conditional Random Field for generating a sequence of gestures that are combined with the robot’s utterance. All the elements presented above conform the core of a modular Human-Robot Interaction architecture that has been integrated in multiple platforms, and tested under different conditions.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fernando Torres Medina.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Amirabdollahian Farshi

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies