Introducing non-linear analysis into sustained speech characterization to improve sleep apnea detection

We present a novel approach for detecting severe obstructive sleep apnea (OSA) cases by introducing non-linear analysis into sustained speech characterization. The proposed scheme was designed for providing additional information into our baseline system, built on top of state-of-the-art cepstral domain modeling techniques, aiming to improve accuracy rates. This new information is lightly correlated with our previous MFCC modeling of sustained speech and uncorrelated with the information in our continuous speech modeling scheme. Tests have been performed to evaluate the improvement for our detection task, based on sustained speech as well as combined with a continuous speech classifier, resulting in a 10% relative reduction in classification for the first and a 33% relative reduction for the fused scheme. Results encourage us to consider the existence of non-linear effects on OSA patients' voices, and to think about tools which could be used to improve short-time analysis

Improving Speech Interaction in Vehicles Using Context-Aware Information through A SCXML Framework

Speech Technologies can provide important benefits for the development of more usable and safe in-vehicle human-machine interactive systems (HMIs). However mainly due robustness issues, the use of spoken interaction can entail important distractions to the driver. In this challenging scenario, while speech technologies are evolving, further research is necessary to explore how they can be complemented with both other modalities (multimodality) and information from the increasing number of available sensors (context-awareness). The perceived quality of speech technologies can significantly be increased by implementing such policies, which simply try to make the best use of all the available resources; and the in vehicle scenario is an excellent test-bed for this kind of initiatives. In this contribution we propose an event-based HMI design framework which combines context modelling and multimodal interaction using a W3C XML language known as SCXML. SCXML provides a general process control mechanism that is being considered by W3C to improve both voice interaction (VoiceXML) and multimodal interaction (MMI). In our approach we try to anticipate and extend these initiatives presenting a flexible SCXML-based approach for the design of a wide range of multimodal context-aware HMI in-vehicle interfaces. The proposed framework for HMI design and specification has been implemented in an automotive OSGi service platform, and it is being used and tested in the Spanish research project MARTA for the development of several in-vehicle interactive applications

An Experimental Platform for large-scale research facing FI-IoT scenarios

Providing experimental facilities for the Internet of Things (IoT) world is of paramount importance to materialise the Future Internet (FI) vision. The level of maturity achieved at the networking level in Sensor and Actuator networks (SAN) justifies the increasing demand on the research community to shift IoT testbed facilities from the network to the service and information management areas. In this paper we present an Experimental Platform fulfilling these needs by: integrating heterogeneous SAN infrastructures in a homogeneous way; providing mechanisms to handle information, and facilitating the development of experimental services. It has already been used to deploy applications in three different field trials: smart metering, smart places and environmental monitoring and it will be one of the components over which the SmartSantander project, that targets a large-scale IoT experimental facility, will rely o

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech

Exploring differences between phonetic classes in Sleep Apnoea Syndrome Patients using automatic speech processing techniques

This work is part of an on-going collaborative project between the medical and signal processing communities to promote new research efforts on automatic OSA (Obstructive Apnea Syndrome) diagnosis. In this paper, we explore the differences noted in phonetic classes (interphoneme) across groups (control/apnoea) and analyze their utility for OSA detectio

Using SCXML to integrate semantic sensor information into context-aware user interfaces

This paper describes a novel architecture to introduce automatic annotation and processing of semantic sensor data within context-aware applications. Based on the well-known state-charts technologies, and represented using W3C SCXML language combined with Semantic Web technologies, our architecture is able to provide enriched higher-level semantic representations of user’s context. This capability to detect and model relevant user situations allows a seamless modeling of the actual interaction situation, which can be integrated during the design of multimodal user interfaces (also based on SCXML) for them to be adequately adapted. Therefore, the final result of this contribution can be described as a flexible context-aware SCXML-based architecture, suitable for both designing a wide range of multimodal context-aware user interfaces, and implementing the automatic enrichment of sensor data, making it available to the entire Semantic Sensor We

GMM-based classifiers for the automatic detection of obstructive sleep apnea

The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach

Smart cities at the forefront of the future internet

Smart cities have been recently pointed out by M2M experts as an emerging market with enormous potential, which is expected to drive the digital economy forward in the coming years. However, most of the current city and urban developments are based on vertical ICT solutions leading to an unsustainable sea of systems and market islands. In this work we discuss how the recent vision of the Future Internet (FI), and its particular components, Internet of Things (IoT) and Internet of Services (IoS), can become building blocks to progress towards a unified urban-scale ICT platform transforming a Smart City into an open innovation platform. Moreover, we present some results of generic implementations based on the ITU-T’s Ubiquitous Sensor Network (USN) model. The referenced platform model fulfills basic principles of open, federated and trusted platforms (FOTs) at two different levels: the infrastructure level (IoT to support the complexity of heterogeneous sensors deployed in urban spaces), and at the service level (IoS as a suit of open and standardized enablers to facilitate the composition of interoperable smart city services). We also discuss the need of infrastructures at the European level for a realistic large-scale experimentally-driven research, and present main principles of the unique-in-the-world experimental test facility under development within the SmartSantander EU project.Although only a few names appear on this paper, this work would not have been possible without the contribution and encouragement of many people, particularly all the enthusiastic team of the SmartSantander project, partially funded by the EC under contract number FP7-ICT-257992

Estabilidad de almacenamiento de ensilados biológicos a partir de residuos de pescado inoculados con bacterias ácido-lácticas

Se elaboraron cuatro muestras por triplicado de ensilados biológicos para alimentación animal a partir de residuos de pescado, utilizando melaza como fuente de carbohidratos para el crecimiento de cuatro cepas de bacterias ácido-lácticas (BAL) aisladas de los mismos, sometidos a un tiempo de incubación de 72 horas y temperatura de 35 °C (±2 °C) para acidificar el producto como método de conservación. A continuación los ensilados se almacenaron durante 180 días a temperatura ambiente para evaluar la estabilidad en anaquel, por medio de análisis químicos, composición química proximal, aminograma, recuentos microbiológicos y algunos de tipo organoléptico del producto terminado. Las cepas fueron eficientes en el proceso de fermentación, causando inhibición del crecimiento de microorganismos indeseables y aportando características organolépticas agradables. El ensilado elaborado con la cepa C14 provocó el descenso del pH en menos de 72 horas de incubación. Ninguno de los productos sufrió deterioro evidente durante el almacenamiento; presentaron porcentajes aceptables de proteína, grasa, cenizas, carbohidratos y aminoácidos, que hacen del producto una fuente utilizable en formulaciones de alimentos para animales.Four biological silages samples for animal feeding were made from fish remains in triplicate, using molasses like source of carbohydrate for the growth of four lactic acid bacteria (LAB) strains isolated from those remains and incubated during 72 hours and temperature of 35°C (±2°C) to acidify the product as preservation method. Then, the silages were storage for 180 days at room temperature to asses the shelf stability by conducting chemical, proximal chemical composition, amine assessment, microbiological counting and some sensory evaluation in final product. Bacteria cultures were efficient in fermentation process causing inhibition growth of undesirable bacteria and giving pleasant sensory characteristics. The silage inoculated with culture C14 made the pH decreased in less than 72 hours of incubation. Neither one of the products suffered clearly deterioration during storage and had acceptable percentages of protein, lipids, ashes, carbohydrates and amino acids; all these make this product as a feasible source to be use in feed animal recipes

Phoneme and Sub-Phoneme T-Normalization for Text-Dependent Speaker Recognition

Test normalization (T-Norm) is a score normalization technique that is regularly and successfully applied in the context of text-independent speaker recognition. It is less frequently applied, however, to text-dependent or textprompted speaker recognition, mainly because its improvement in this context is more modest. In this paper we present a novel way to improve the performance of T-Norm for text-dependent systems. It consists in applying score TNormalization at the phoneme or sub-phoneme level instead of at the sentence level. Experiments on the YOHO corpus show that, while using standard sentence-level T-Norm does not improve equal error rate (EER), phoneme and sub-phoneme level T-Norm produce a relative EER reduction of 18.9% and 20.1% respectively on a state-of-the-art HMM based textdependent speaker recognition system. Results are even better for working points with low false acceptance rates