121 research outputs found
The creation and the therapeutic space in prison
El objetivo de este artÃculo es describir la experiencia que se llevó a cabo en el centro penitenciario Madrid III, Valdemoro, dentro del Programa Marco para la Atención Integral a Enfermos Mentales en Centros Penitenciarios (PAIEM) dependiente de la Subdirección General de Sanidad Penitenciaria. Dicha experiencia tuvo lugar entre los meses de febrero y junio de 2008; respondÃa a una demanda institucional en relación al aumento de enfermos mentales en prisión y a sus necesidades especÃficas; y se concretó a través de un conjunto de talleres arteterapia, a desarrollar dentro del centro, por estudiantes en prácticas del Master de Arteterapia de la UCM.The objective of this article is to describe the experience that was carried out in the penitentiary center Madrid III, Valdemoro, within the Framework Program for the Integral Attention to the Mentally Ill in Prisons (PAIEM) dependent on the General Branch of Health Prison. That experience took place between the months of February and June of 2008; responded to an institutional demand in relation to the increase of mentally ill in prison and your specific needs; and materialized through a series of art therapy workshops, to develop within the center, by students in practices of Master in Art therapy of the UCM
Bases genéticas en la población española con hipertensión arterial pulmonar idiopática o hereditaria y enfermedad venooclusiva pulmonar. Implicaciones clÃnicas actuales
La hipertensión arterial pulmonar (HAP) es una enfermedad rara caracterizada por el remodelado de pequeñas arteriolas pulmonares que conduce al aumento progresivo de la resistencia vascular pulmonar, fracaso ventricular derecho y eventual muerte. Se asocia con diversas condiciones clÃnicas, pudiendo presentarse como formas idiopáticas y hereditarias, habitualmente de presentación en la edad adulta joven.
Durante muchos años el gen BMPR2 ha sido el único conocido relacionado con el desarrollo de HAP hereditaria, de herencia autosómico dominante, penetrancia incompleta y expresividad variable, caracterizándose por formas tempranas de presentación, mayor severidad hemodinámica y curso clÃnico más grave.
Sin embargo, recientes avances en el campo de la genética han permitido el descubrimiento de nuevos genes responsables, como KCNK3 y TBX4, entre otros, cuyos fenotipos clÃnicos asociados han sido escasamente descritos.
Por otro lado, la enfermedad venooclusiva pulmonar (EVOP) hereditaria es una forma rara de HAP consistente en la afectación predominante de las vénulas pulmonares, que conduce de manera análoga a un aumento de la resistencia vascular pulmonar, fallo ventricular derecho y muerte. Se caracteriza por la reducción de la capacidad de difusión del monóxido de carbono, edades tempranas de presentación y un curso clÃnico agresivo, con mala respuesta al tratamiento con vasodilatadores pulmonares con eventual desarrollo de edema pulmonar. Hasta la fecha sólo se ha descrito un gen relacionado con el desarrollo de EVOP hereditaria, el gen EIF2AK4, de herencia autosómica recesiva y elevada penetrancia.
La población española con HAP idiopática y hereditaria ha sido escasamente descrita, siendo desconocido el papel de las distintas alteraciones genéticas. Por otro lado, no existen estudios previos en nuestro paÃs referentes a la EVOP hereditaria.
La presente Tesis Doctoral recoge la mayor cohorte de pacientes con HAP idiopática y hereditaria estudiada en nuestro paÃs y la única de pacientes con EVOP hereditaria, de etnia gitana y marcada consanguineidad, incluidos en el marco del Estudio Multicéntrico Español de genética de HAP. El objetivo es analizar la prevalencia de las distintas alteraciones genéticas en la población española de HAP idiopática y hereditaria, estudiar genéticamente la población española de etnia gitana con EVOP hereditaria y describir el fenotipo clÃnico y pronóstico asociado a cada una de las alteraciones genéticas estudiadas, asà como llevar a cabo el cribado de familiares.
A pesar de los avances experimentados en materia de diagnóstico y tratamiento, el pronóstico de ambas entidades sigue siendo muy pobre con una breve supervivencia libre de muerte o trasplante pulmonar, a dÃa de hoy el único tratamiento eficaz. Quizás el creciente conocimiento en el campo de la genética permita en un futuro, no sólo el diagnóstico precoz y consejo reproductivo dirigido a prevenir la aparición de nuevos casos, sino además, el diseño de un plan terapéutico individualizado para cada paciente en función del perfil genético hallado, que permita optimizar los recursos y los resultados clÃnicos obtenidos
La creación y el espacio terapéutico en el medio penitenciario
El objetivo de este artÃculo es describir la experiencia que se llevó a cabo en el centro penitenciario Madrid III, Valdemoro, dentro del Programa Marco para la Atención Integral a Enfermos Mentales en Centros Penitenciarios (PAIEM) dependiente de la Subdirección General de Sanidad Penitenciaria. Dicha experiencia tuvo lugar entre los meses de febrero y junio de 2008; respondÃa a una demanda institucional en relación al aumento de enfermos mentales en prisión y a sus necesidades especÃficas; y se concretó a través de un conjunto de talleres arteterapia, a desarrollar dentro del centro, por estudiantes en prácticas del Master de Arteterapia de la UCM
Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations
Query-by-example spoken term detection (QbE STD) aims at retrieving data from a speech repository given an acoustic query containing the term of interest as input. Nowadays, it is receiving much interest due to the large volume of multimedia information. This paper presents the systems submitted to the ALBAYZIN QbE STD 2014 evaluation held as a part of the ALBAYZIN 2014 Evaluation campaign within the context of the IberSPEECH 2014 conference. This is the second QbE STD evaluation in Spanish, which allows us to evaluate the progress in this technology for this language. The evaluation consists in retrieving the speech files that contain the input queries, indicating the start and end times where the input queries were found, along with a score value that reflects the confidence given to the detection of the query. Evaluation is conducted on a Spanish spontaneous speech database containing a set of talks from workshops, which amount to about 7 h of speech. We present the database, the evaluation metric, the systems submitted to the evaluation, the results, and compare this second evaluation with the first ALBAYZIN QbE STD evaluation held in 2012. Four different research groups took part in the evaluations held in 2012 and 2014. In 2014, new multi-word and foreign queries were added to the single-word and in-language queries used in 2012. Systems submitted to the second evaluation are hybrid systems which integrate letter transcription- and template matching-based systems. Despite the significant improvement obtained by the systems submitted to this second evaluation compared to those of the first evaluation, results still show the difficulty of this task and indicate that there is still room for improvement.This research was funded by the Spanish Government ('SpeechTech4All Project' TEC2012 38939 C03 01 and 'CMC-V2 Project' TEC2012 37585 C02 01), the Galician Government through the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014) and 'AtlantTIC Project' CN2012/160, and also by the Spanish Government and the European Regional Development Fund (ERDF) under project TACTICA
Analysis of thermal comfort in nursing homes in the Atlantic climate
A esperança média de vida da população mundial tem vindo a aumentar,
incrementando a faixa etária acima dos 65 anos. Nesse sentido, determinados
ser viços experimentam maior demanda em resposta às necessidades
crescentes desta população, como é o caso dos ser viços de
cuidados a longo prazo, nomeadamente, centros de dia (CD) e estruturas
residenciais para idosos (ERPI). Neste âmbito de ser viços para a comunidade
idosa, os parâmetros de qualidade e confor to são apresentados
como fatores cruciais para o bem-estar dos utentes/residentes, sendo
que o confor to térmico (CT) é um fator determinante na monitorização do
bem-estar desta população. Para que os valores ótimos de CT sejam alcançados
e mantidos nos edificados com esta tipologia de ser viços, elevados
gastos energéticos são despendidos para retificar as condições
estruturais dos edifÃcios. A fim de estruturar um modelo matemático viável
que permita definir as caracterÃsticas estruturais otimizadas na fase
de construção e reabilitação dos edifÃcios para CD ou ERPI, torna-se necessário
analisar o CT dos utentes e prever quais as condições térmicas
aceitáveis ou preferenciais para esta população. Este estudo, ainda em
curso e integrante do programa ConTerMa, analisa as variáveis de CT na
zona climática continental atlântica, monitorizando 8 ERPI e CD situados
em 5 concelhos da área metropolitana do Por to, e 5 ERPI e CD na área
metropolitana de Barcelona, representativas do clima mediterrânico.In recent years, the average life expectancy of the world's population has
been rising, resulting in a steady increase in the elderly population. With
the older age group increasing, certain ser vices are in greater demand
in response to the growing needs of this population, such as the case of
long-term care ser vices, i.e. Day Centres (DC) and Nursing Homes (NH).
In this ser vice area, quality and comfort parameters are presented as crucial
factors for the well-being of the users, with thermal comfort being one
of the most important quality parameters of well-being of this population.
It takes high energy costs in order for optimum values of thermal comfort
(TC) to be achieved and maintained in buildings built for this type of ser vice,
since structural conditions of buildings are often degraded. In order to
structure a viable mathematical model that allows to define the optimized
structural characteristics in the construction phase of the buildings for
permanent or temporary geriatric residences, it is necessary to analyse, in
an initial phase, the TC of the users of this type of ser vice and to predict
which are the acceptable or preferred thermal conditions for this population.
The TC analyses, in this study, will focus on an area of continental
Mediterranean climate, addressing 8 NH and DC in the metropolitan area
of Porto, representative of the Atlantic climate zone in Portugal, and 5 NH
and DC in the metropolitan area of Barcelona, exemplifying of the Mediterranean
climate.Projeto financiado ao abrigo do Anúncio de Seleção de Trabalhos
de Investigação Multidisciplinar sobre o Envelhecimento ‘Fondo
Europeo de Desarrollo Regional en el marco del Programa de Cooperación
Interreg V-A España – Portugal, (POCTEP) 2014-2020,
Expediente: 6/2018_CIE_6’, no âmbito do Programa Coordenado
‘ConTerMa- Análisis del confort térmico en residencias de ancianos
en el espacio de cooperación transfronterizo de España-Portugal’.info:eu-repo/semantics/publishedVersio
Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation
[Abstract] The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.Centro singular de investigación de Galicia; ED431G/04Universidad del PaÃs Vasco; GIU16/68Ministerio de EconomÃa y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, Innovación y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0
A one health approach
This work was funded by the R&D Project CAREBIO2 - Comparative assessment of antimicrobial resistance in environmental biofilms through proteomics - towards innovative theranostic biomarkers, with reference NORTE-01-0145-FEDER-030101 and PTDC/SAU-INF/30101/2017, financed by the European Regional Development Fund (ERDF) through the Northern Regional Operational Program (NORTE 2020) and the Foundation for Science and Technology (FCT). This work was supported by the Associate Laboratory for Green Chemistry - LAQV which is financed by national funds from FCT/MCTES (UID/QUI/50006/2019). Vanessa Silva is supported by national funds through FCT/MCTES and by the European Social Fund through POCH/FSE under the PhD grant SFRH/BD/137947/2018.publishersversionpublishe
ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish
[Abstract] Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain.Ministerio de EconomÃa y Competitividad; TIN2015-64282-R,Ministerio de EconomÃa y Competitividad; RTI2018-093336-B-C22Ministerio de EconomÃa y Competitividad; TEC2015-65345-PXunta de Galicia; ED431B 2016/035Xunta de Galicia; GPC ED431B 2019/003Xunta de Galicia; GRC 2014/024Xunta de Galicia; ED431G/01Xunta de Galicia; ED431G/04Agrupación estratéxica consolidada; GIU16/68Ministerio de EconomÃa y Competitividad; TEC2015-68172-C2-1-
ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation
[EN] Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input. This paper presents the systems submitted to the ALBAYZIN QbE STD 2016 Evaluation held as a part of the ALBAYZIN 2016 Evaluation Campaign at the IberSPEECH 2016 conference. Special attention was given to the evaluation design so that a thorough post-analysis of the main results could be carried out. Two different Spanish speech databases, which cover different acoustic and language domains, were used in the evaluation: the MAVIR database, which consists of a set of talks from workshops, and the EPIC database, which consists of a set of European Parliament sessions in Spanish. We present the evaluation design, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a thorough analysis and discussion. Four different research groups participated in the evaluation, and a total of eight template matching-based systems were submitted. We compare the systems submitted to the evaluation and make an in-depth analysis based on some properties of the spoken queries, such as query length, single-word/multi-word queries, and in-language/out-of-language queries.This work was partially supported by Fundacao para a Ciencia e Tecnologia (FCT) under the projects UID/EEA/50008/2013 (pluriannual funding in the scope of the LETSREAD project) and UID/CEC/50021/2013, and Grant SFRH/BD/97187/2013. Jorge Proenca is supported by the SFRH/BD/97204/2013 FCT Grant. This work was also supported by the Galician Government ('Centro singular de investigacion de Galicia' accreditation 2016-2019 ED431G/01 and the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014)), the European Regional Development Fund (ERDF), the projects "DSSL: Redes Profundas y Modelos de Subespacios para Deteccion y Seguimiento de Locutor, Idioma y Enfermedades Degenerativas a partir de la Voz" (TEC2015-68172-C2-1-P) and the TIN2015-64282-R funded by Ministerio de Economia y Competitividad in Spain, the Spanish Government through the project "TraceThem" (TEC2015-65345-P), and AtlantTIC ED431G/04.Tejedor, J.; Toledano, DT.; Lopez-Otero, P.; Docio-Fernandez, L.; Proença, J.; Perdigão, F.; GarcÃa-Granada, F.... (2018). ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation. EURASIP Journal on Audio, Speech and Music Processing. 1-25. https://doi.org/10.1186/s13636-018-0125-9S125Jarina, R, Kuba, M, Gubka, R, Chmulik, M, Paralic, M (2013). UNIZA system for the spoken web search task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 791–792).Ali, A, & Clements, MA (2013). Spoken web search using and ergodic hidden Markov model of speech. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 861–862).Buzo, A, Cucu, H, Burileanu, C (2014). SpeeD@MediaEval 2014: Spoken term detection with robust multilingual phone recognition. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–722).Caranica, A, Buzo, A, Cucu, H, Burileanu, C (2015). SpeeD@MediaEval 2015: Multilingual phone recognition approach to Query By Example STD. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 781–783).Kesiraju, S, Mantena, G, Prahallad, K (2014). IIIT-H system for MediaEval 2014 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 761–762).Ma, M, & Rosenberg, A (2015). CUNY systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–833).Takahashi, J, Hashimoto, T, Konno, R, Sugawara, S, Ouchi, K, Oshima, S, Akyu, T, Itoh, Y (2014). An IWAPU STD system for OOV query terms and spoken queries. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 384–389).Makino, M, & Kai, A (2014). Combining subword and state-level dissimilarity measures for improved spoken term detection in NTCIR-11 SpokenQuery & Doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 413–418).Konno, R, Ouchi, K, Obara, M, Shimizu, Y, Chiba, T, Hirota, T, Itoh, Y (2016). An STD system using multiple STD results and multiple rescoring method for NTCIR-12 SpokenQuery & Doc task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 200–204).Sakamoto, N, Yamamoto, K, Nakagawa, S (2015). Combination of syllable based N-gram search and word search for spoken term detection through spoken queries and IV/OOV classification. In Proc. of ASRU. IEEE, New York, (pp. 200–206).Hou, J, Pham, VT, Leung, C-C, Wang, L, 2, HX, Lv, H, Xie, L, Fu, Z, Ni, C, Xiao, X, Chen, H, Zhang, S, Sun, S, Yuan, Y, Li, P, Nwe, TL, Sivadas, S, Ma, B, Chng, ES, Li, H (2015). The NNI Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 141–143).Vavrek, J, Viszlay, P, Lojka, M, Pleva, M, Juhar, J, Rusko, M (2015). TUKE at MediaEval 2015 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 451–453).Mantena, G, Achanta, S, Prahallad, K (2014). Query-by-example spoken term detection using frequency domain linear prediction and non-segmental dynamic time warping. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(5), 946–955.Anguera, X, & Ferrarons, M (2013). Memory efficient subsequence DTW for query-by-example spoken term detection. In Proc. of ICME. IEEE, New York, (pp. 1–6).Tulsiani, H, & Rao, P (2015). The IIT-B Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 341–343).Bouallegue, M, Senay, G, Morchid, M, Matrouf, D, Linares, G, Dufour, R (2013). LIA@MediaEval 2013 spoken web search task: An I-Vector based approach. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 771–772).Rodriguez-Fuentes, LJ, Varona, A, Penagarikano, M, Bordel, G, Diez, M (2013). GTTS systems for the SWS task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–832).Wang, H, Lee, T, Leung, C-C, Ma, B, Li, H (2013). Using parallel tokenizers with DTW matrix combination for low-resource spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 8545–8549).Wang, H, & Lee, T (2013). The CUHK spoken web search system for MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 681–682).Proenca, J, Veiga, A, Perdigão, F (2014). The SPL-IT query by example search on speech system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 741–742).Proenca, J, Veiga, A, Perdigao, F (2015). Query by example search with segmented dynamic time warping for non-exact spoken queries. In Proc. of EUSIPCO. Springer, Berlin, (pp. 1691–1695).Proenca, J, Castela, L, Perdigao, F (2015). The SPL-IT-UC Query by Example search on speech system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 471–473).Proenca, J, & Perdigao, F (2016). Segmented dynamic time warping for spoken Query-by-Example search. In Proc. of Interspeech. ISCA, Baixas, (pp. 750–754).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). GTM-UVigo systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 521–523).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). Phonetic unit selection for cross-lingual Query-by-Example spoken term detection. In Proc. of ASRU. IEEE, New York, (pp. 223–229).Saxena, A, & Yegnanarayana, B (2015). Distinctive feature based representation of speech for Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 3680–3684).Skacel, M, & Szöke, I (2015). BUT QUESST 2015 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–723).Chen, H, Leung, C-C, Xie, L, Ma, B, Li, H (2016). Unsupervised bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 923–927).Yuan, Y, Leung, C-C, Xie, L, Chen, H, Ma, B, Li, H (2017). Pairwise learning using multi-lingual bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 5645–5649).Torbati, AHHN, & Picone, J (2016). A nonparametric Bayesian approach for spoken term detection by example query. In Proc. of Interspeech. ISCA, Baixas, (pp. 928–932).Popli, A, & Kumar, A (2015). Query-by-example spoken term detection using low dimensional posteriorgrams motivated by articulatory classes. In Proc. of MMSP. IEEE, New York, (pp. 1–6).Yang, P, Leung, C-C, Xie, L, Ma, B, Li, H (2014). Intrinsic spectral analysis based on temporal context features for query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 1722–1726).George, B, Saxena, A, Mantena, G, Prahallad, K, Yegnanarayana, B (2014). Unsupervised query-by-example spoken term detection using bag of acoustic words and non-segmental dynamic time warping. In Proc. of Interspeech. ISCA, Baixas, (pp. 1742–1746).Hazen, TJ, Shen, W, White, CM (2009). Query-by-example spoken term detection using phonetic posteriorgram templates. In Proc. of ASRU. IEEE, New York, (pp. 421–426).Abad, A, Astudillo, RF, Trancoso, I (2013). The L2F spoken web search system for mediaeval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 851–852).Szöke, I, Skácel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Szöke, I, Burget, L, Grézl, F, ÄŒernocký, JH, Ondel, L (2014). Calibration and fusion of query-by-example systems - BUT SWS 2013. In Proc. of ICASSP. IEEE, New York, (pp. 621–622).Abad, A, RodrÃguez-Fuentes, LJ, Penagarikano, M, Varona, A, Bordel, G (2013). On the calibration and fusion of heterogeneous spoken term detection systems. In Proc. of Interspeech. ISCA, Baixas, (pp. 20–24).Yang, P, Xu, H, Xiao, X, Xie, L, Leung, C-C, Chen, H, Yu, J, Lv, H, Wang, L, Leow, SJ, Ma, B, Chng, ES, Li, H (2014). The NNI query-by-example system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 691–692).Leung, C-C, Wang, L, Xu, H, Hou, J, Pham, VT, Lv, H, Xie, L, Xiao, X, Ni, C, Ma, B, Chng, ES, Li, H (2016). Toward high-performance language-independent Query-by-Example spoken term detection for MediaEval 2015: Post-evaluation analysis. In Proc. of Interspeech. ISCA, Baixas, (pp. 3703–3707).Xu, H, Hou, J, Xiao, X, Pham, VT, Leung, C-C, Wang, L, Do, VH, Lv, H, Xie, L, Ma, B, Chng, ES, Li, H (2016). Approximate search of audio queries by using DTW with phone time boundary and data augmentation. In Proc. of ICASSP. IEEE, New York, (pp. 6030–6034).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level and DNN-based acoustic matches for efficient spoken term detection in NTCIR-12 SpokenQuery &Doc-2 task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 205–210).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level spotting and posterior-based acoustic match for improved query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 740–744).Obara, M, Kojima, K, Tanaka, K, Lee, S-w, Itoh, Y (2016). Rescoring by combination of posteriorgram score and subword-matching score for use in Query-by-Example. In Proc. of Interspeech. ISCA, Baixas, (pp. 1918–1922).NIST. The Ninth Text REtrieval Conference (TREC 9). http://trec.nist.gov . Accessed Feb 2018.Anguera, X, Rodriguez-Fuentes, LJ, Szöke, I, Buzo, A, Metze, F (2014). Query by Example Search on Speech at Mediaeval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 351–352).Joho, H, & Kishida, K (2014). Overview of the NTCIR-11 SpokenQuery&Doc Task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–7).NIST. Draft KWS16 Keyword Search Evaluation Plan. https://www.nist.gov/sites/default/files/documents/itl/iad/mig/KWS16-evalplan-v04.pdf . Accessed Feb 2018.Anguera, X, Metze, F, Buzo, A, Szöke, I, Rodriguez-Fuentes, LJ (2013). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 921–922).Taras, B, & Nadeu, C (2011). Audio segmentation of broadcast news in the Albayzin-2010 evaluation: overview, results, and discussion. EURASIP Journal on Audio, Speech, and Music Processing, 2011(1), 1–10.Zelenák, M, Schulz, H, Hernando, J (2012). Speaker diarization of broadcast news in Albayzin 2010 evaluation campaign. EURASIP Journal on Audio, Speech, and Music Processing, 2012(19), 1–9.RodrÃguez-Fuentes, LJ, Penagarikano, M, Varona, A, DÃez, M, Bordel, G (2011). The Albayzin 2010 Language Recognition Evaluation. In Proc. of Interspeech. ISCA, Baixas, (pp. 1529–1532).Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C, Cardenal, A, Echeverry-Correa, JD, Coucheiro-Limeres, A, Olcoz, J, Miguel, A (2015). Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech and Music Processing, 2015(21), 1–27.Tejedor, J, Toledano, DT, Anguera, X, Varona, A, Hurtado, LF, Miguel, A, Colás, J (2013). Query-by-example spoken term detection ALBAYZIN 2012 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech, and Music Processing, 2013(23), 1–17.Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations. EURASIP, Journal on Audio, Speech and Music Processing, 2016(1), 1–19.Méndez, F, DocÃo, L, Arza, M, Campillo, F (2010). The Albayzin 2010 text-to-speech evaluation. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 317–340).Billa, J, Ma, KW, McDonough, JW, Zavaliagkos, G, Miller, DR, Ross, KN, El-Jaroudi, A (1997). Multilingual speech recognition: the 1996 Byblos Callhome system. In Proc. of Eurospeech. ISCA, Baixas, (pp. 363–366).Killer, M, Stuker, S, Schultz, T (2003). Grapheme based speech recognition. In Proc. of Eurospeech. ISCA, Baixas, (pp. 3141–3144).Burget, L, Schwarz, P, Agarwal, M, Akyazi, P, Feng, K, Ghoshal, A, Glembek, O, Goel, N, Karafiat, M, Povey, D, Rastrow, A, Rose, RC, Thomas, S (2010). Multilingual acoustic modeling for speech recognition based on subspace gaussian mixture models. In Proc. of ICASSP. IEEE, New York, (pp. 4334–4337).Cuayahuitl, H, & Serridge, B (2002). Out-of-vocabulary word modeling and rejection for Spanish keyword spotting systems. In Proc. of MICAI. Springer, Berlin, (pp. 156–165).Tejedor, J (2009). Contributions to keyword spotting and spoken term detection for information retrieval in audio mining. PhD thesis, Universidad Autónoma de Madrid, Madrid, Spain.Tejedor, J, Toledano, DT, Wang, D, King, S, Colás, J (2014). Feature analysis for discriminative confidence estimation in spoken term detection. Computer Speech and Language, 28(5), 1083–1114.Li, J, Wang, X, Xu, B (2014). An empirical study of multilingual and low-resource spoken term detection using deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 1747–1751).NIST. The Spoken Term Detection (STD) 2006 evaluation plan. http://berlin.csie.ntnu.edu.tw/Courses/Special%20Topics%20in%20Spoken%20Language%20Processing/Lectures2008/SLP2008S-Lecture12-Spoken%20Term%20Detection.pdf . Accessed Feb 2018.Fiscus, JG, Ajot, J, Garofolo, JS, Doddingtion, G (2007). Results of the 2006 spoken term detection evaluation. In Proc. of SSCS. ACM, New York, (pp. 45–50).Martin, A, Doddington, G, Kamm, T, Ordowski, M, Przybocki, M (1997). The DET curve in assessment of detection task performance. In Proc. of Eurospeech. ISCA, Baixas, (pp. 1895–1898).NIST. Evaluation Toolkit (STDEval) software. https://www.nist.gov/itl/iad/mig/tools . Accessed Feb 2018.Union, IT. ITU-T Recommendation P.563: Single-ended method for objective speech quality assessment in narrow-band telephony applications. http://www.itu.int/rec/T-REC-P.563/en . Accessed Feb 2018.Rajput, N, & Metze, F (2011). Spoken web search. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 1–2).Metze, F, Barnard, E, Davel, M, van Heerden, C, Anguera, X, Gravier, G, Rajput, N (2012). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 41–42).Szöke, I, Rodriguez-Fuentes, LJ, Buzo, A, Anguera, X, Metze, F, Proenca, J, Lojka, M, Xiong, X (2015). Query by Example Search on Speech at Mediaeval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Szöke, I, & Anguera, X (2016). Zero-cost speech recognition task at Mediaeval 2016. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2014). Overview of the NTCIR-11 spokenquery &doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–15).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2016). Overview of the NTCIR-12 spokenquery &doc-2. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 1–13).Schwarz, P (2008). Phoneme recognition based on long temporal context. PhD thesis, FIT, BUT, Brno, Czech Republic.Varona, A, Penagarikano, M, RodrÃguez-Fuentes, LJ, Bordel, G (2011). On the use of lattices of time-synchronous cross-decoder phone co-occurrences in a SVM-phonotactic language recognition system. In Proc. of Interspeech. ISCA, Baixas, (pp. 2901–2904).Eyben, F, Wollmer, M, Schuller, B (2010). OpenSMILE—the munich versatile and fast open-source audio feature extractor. In Proc. of ACM Multimedia (MM). ACM, New York, (pp. 1459–1462).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Finding relevant features for zero-resource query-by-example search on speech. Speech Communication, 84(1), 24–35.Zhang, Y, & Glass, JR (2009). Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams. In Proc. of ASRU. IEEE, New York, (pp. 398–403).Povey, D, Ghoshal, A, Boulianne, G, Burget, L, Glembek, O, Goel, N, Hannemann, M, Motlicek, P, Qian, Y, Schwarz, P, Silovsky, J, Stemmer, G, Vesely, K (2011). The KALDI speech recognition toolkit. In Proc. of ASRU. IEEE, New York, (pp. 1–4).Muller, M. (2007). Information retrieval for music and motion. New York: Springer.Szöke, I, Skacel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Brümmer, N, & van Leeuwen, D (2006). On calibration of language recognition scores. In Proc of the IEEE Odyssey: The speaker and language recognition workshop. IEEE, New York, (pp. 1–8).Brümmer, N, & de Villiers, E. The BOSARIS toolkit user guide: Theory, algorithms and code for binary classifier score processing. Technical report. https://sites.google.com/site/nikobrummer . Accessed Feb 2018.Meinedo, H, & Neto, J (2005). A stream-based audio segmentation, classification and clustering pre-processing system for broadcast news using ANN models. In Proc. of Interspeech. ISCA, Baixas, (pp. 237–240).Morgan, N, & Bourlard, H (1995). An introduction to hybrid HMM/connectionist continuous speech recognition. IEEE Signal Processing Magazine, 12(3), 25–42.Meinedo, H, Abad, A, Pellegrini, T, Trancoso, I, Neto, J (2010). The L2F broadcast news speech recognition system. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 93–96).Abad, A, Luque, J, Trancoso, I (2011). Parallel transformation network features for speaker recognition. In Proc. of ICASSP. IEEE, New York, (pp. 5300–5303).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2012). On the use of phone log-likelihood ratios as features in spoken language recognition. In Proc. of SLT. IEEE, New York, (pp. 274–279).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2014). New insight into the use of phone log-likelihood ratios as features for language recognition. In Proc. of Interspeech. ISCA, Baixas, (pp. 1841–1845).Abad, A, Ribeiro, E, Kepler, F, Astudillo, R, Trancoso, I (2016). Exploiting phone log-likelihood ratio features for the detection of the native language of non-native English speakers. In Proc. of Interspeech. ISCA, Baixas, (pp. 2413–2417).RodrÃguez-Fuentes, LJ, Varona, A, Peñagarikano, M, Bordel, G, DÃez, M (2014). High-performance query-by-example spoken term detection on the SWS 2013 evaluation. In Proc. of ICASSP. IEEE, New York, (pp. 7819–7823).Vesely, K, Ghoshal, A, Burget, L, Povey, D (2013). Sequence-discriminative training of deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 2345–2349).Ghahremani, P, BabaAli, B, Povey, D, Riedhammer, K, Trmal, J, Khudanpur, S (2014). A pitch extraction algorithm tuned for automatic speech recognition. In Proc. of ICASSP. IEEE, New York, (pp. 2494–2498).Povey, D, Hannemann, M, Boulianne, G, Burget, L, Ghoshal, A, Janda, M, Karafiat, M, Kombrink, S, Motlicek, P, Qian, Y, Riedhammer, K, Vesely, K, Vu, NT (2012). Generating exact lattices in the WFST framework. In Proc. of ICASSP. IEEE, New York, (pp. 4213–4216).Garcia-Mateo, C, Dieguez-Tirado, J, Docio-Fernandez, L, Cardenal-Lopez, A (2004). Transcrigal: A bilingual system for automatic indexing of broadcast news. In Proc. of LREC. ELRA, Paris, (pp. 2061–2064).Stolcke, A (2002). SRILM—an extensible language modeling toolkit. In Proc. of Interspeech. ISCA, Baixas, (pp. 901–904).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). GTM-UVigo systems for Albayzin 2016 search on speech evaluation. In Proc. of Iberspeech. Springer, Berlin, (pp. 65–74).Chen, G, Khudanpur, S, Povey, D, Trmal, J, Yarowsky, D, Yilmaz, O (2013). Quantifying the value of pronunciation lexicons for keyword search in low resource languages. In Proc. of ICASSP. IEEE, New York, (pp. 8560–8564).Pham, VT, Chen, NF, Sivadas, S, Xu, H, Chen, I-F, Ni, C, Chng, ES, Li, H (2014). System and keyword dependent fusion for spoken term detection. In Proc. of SLT. IEEE, New York, (pp. 430–435).Can, D, & Saraclar, M (2011). Lattice indexing for spoken term detection. IEEE Transactions on Audio, Speech and Language Processing, 19(8), 2338–2347.Miller, DRH, K
Impact of Pain Neuroscience Education Program in Community Physiotherapy Context on Pain Perception and Psychosocial Variables Associated with It in Elderly Persons: A Ranzomized Controlled Trial
This study investigated the long-term effect (six-months) of a Pain Neuroscience Education
(PNE) program on pain perception, quality of life, kinesiophobia and catastrophism in older adults
with multimorbidity and chronic pain. Fifty participants (n = 50) were randomly assigned to the
pain education therapy group (PET; n = 24) and control group (CG; n = 26). The PET group received
six sessions (i.e., once a week, 50 min) about neurophysiology of pain while the CG carried on with
their usual life. Perception of pain through the visual analogue scale (VAS), quality of life (EQ-5D
questionnaire), kinesiophobia (TSK-11) and catastrophism (PCS) were assessed after six months
since the last PNE session. Statistically significant differences on VAS (t(48) = 44, p = 0.01, ES = 0.42
[0.13, 0.65]) was found in favor to PET group. No other statistically significant differences were found.
This study found that the application of a PNE intervention in an isolated form was able to significantly reduce pain perception with low effect size in the long-term (six months after intervention) in
elderly people with chronic pain.Medicin
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