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 País Vasco; GIU16/68Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, Innovación y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0

Query-by-Example Keyword Spotting

Tato diplomová práce se zabývá moderními přístupy detekce klíčových slov a detekce frází v řečových datech. V úvodní části je seznámení s problematikou a teoretický popis metod pro detekci. Následuje popis reprezentace vstupních datových sad použitých při experimentech a evaluaci. Dále jsou uvedeny metody pro detekci klíčových slov definovaných vzorem. Následně jsou popsány evaluační metody a techniky použité pro skórování. Po provedení experimentů na datových sadách a po evaluaci jsou diskutovány výsledky. V dalším kroku jsou navrženy a poté implementovány moderní postupy vedoucí k vylepšení systému pro detekci a opět je provedena evaluace a diskuze dosažených výsledků. V závěrečné části je práce zhodnocena a jsou zde navrženy další směy vývoje našeho systému. Příloha obsahuje manuál pro používání implementovaných skriptů.The aim of the thesis is to get acquainted with modern approach of keyword spotting and spoken term detection in speech data. The bases of keyword spotting are described at first. The data representation used for experiments and evaluation are introduced. Keyword spotting methods where query is provided as an audio example (Query-by-Example) are presented. The scoring metrics are described and experiments follow. The results are discussed. Further, modern approaches of keyword spotting are suggested and implemented. The system with new techniques is evaluated and the discussion of results achieved follows. The conclusions are drawn and the discussion of future directions of development is held. The Appendix contains user manual for using implemented system.

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.; García-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). 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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

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 Economía y Competitividad; TIN2015-64282-R,Ministerio de Economía y Competitividad; RTI2018-093336-B-C22Ministerio de Economía 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 Economía y Competitividad; TEC2015-68172-C2-1-

Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms

Detección de palabras clave en voz mediante ejemplos empleando redes neuronales profundas

Actualmente nos vemos inmersos en un mundo donde los datos multimedia son cada vez más cuantiosos y frecuentes. Con el objetivo de extraer información y detectar palabras clave en ficheros de audio presentes en medios de comunicación e Internet, entre otras aplicaciones como la interacción con sistemas sin teclado o búsquedas para personas ciegas, surgen los sistemas QbE-STD (Query-by-Example Spoken Term Detecion). Estos sistemas tienen como objetivo, buscar un ejemplo de un objeto o parte de él en otro objeto, que aplicado a nuestro trabajo consiste, en el reconocimiento de palabras o secuencias de ellas en archivos de audio. En este Trabajo Fin de Grado se ha tomado como punto de partida el Trabajo Fin de Máster con el título de “Implementación y evaluación de un sistema QbE-STD (Query-by-Example Spoken Term Detection)” de María Cabello Aguilar con el fin de desarrollar un nuevo módulo, donde emplearemos redes neuronales profundas, que servirá para mejorar los resultados obtenidos en las últimas evaluaciones Albayzin 2016 y 2018 Search on Speech. Al igual que anteriormente nuestro sistema deberá realizar la correcta detección independiente del idioma de la entrada o query, basada en términos hablados. Llegando incluso a ser posible que un usuario realice una búsqueda en un repositorio de audio emitiendo con su voz el término a buscar. La técnica empleada para representar estos términos hablados ha sido la de posteriorgramas fonéticos. Estos posteriorgramas se han obtenido haciendo uso de los decodificadores fonéticos desarrollados por la Universidad de Tecnología de Brno (BUT), empleandose también el kit de herramientas de modelos ocultos de Markov oculto (HTK) para la correcta utilización de estos posteriorgramas. Para realizar la detección de los terminos hablados en los correspondientes repositorios de audio se ha empleado las ya mencionadas redes neuronales profundas. Previo a esto se realizó un exahustivo trabajo de tratamiento de la base de datos con el fin de poder adaptar el material disponible a este nuevo módulo. De esta manera conseguimos desarrollar un sistema que puede servir como punto de partida para futuras vías de trabajo del grupo AUDIAS. 1 Para el desarrollo de la solución y la realización de las pruebas se han utilizado los audios pertenecientes a las evaluaciones Albayzin 2016 y 2018 Search on Speech, mencionado anteriormente. Con la intención de obtener resultados que se puedan contrastar con otros sistemas publicados similares pudiendo llegar a ser competitivos y semejantes a los de otras implementaciones parecidas

Implementación y evaluación de un sistema QbE-STD (Query-by-Example Spoken Term Detection)

Con el fin de extraer información y reconocer palabras clave en los ficheros de audio presentes en medios de comunicación e Internet, surgen los sistemas QbE-STD (Query-by- Example Spoken Term Detecion). Los sistemas QbE-STD tratan, por un lado de buscar un ejemplo de un objeto o parte de él en otro objeto (QbE), y por otro de encontrar palabras o secuencias de ellas en archivos de audio (STD). En este Trabajo Fin de Máster se ha desarrollado un sistema QbE-STD independiente del idioma cuya entrada o query está basada en términos hablados, lo que permite a un usuario realizar una búsqueda en un repositorio de audio emitiendo con su voz el término a buscar. Como técnica de representación del habla se han empleado los llamados posteriorgramas fonéticos, obtenidos mediante los decodificadores fonéticos desarrollados por la Universidad de Tecnología de Brno (BUT). Para la detección de los términos de búsqueda en los repositorios de audio se ha utilizado el algoritmo Subsequence Dynamic Time Warping (S-DTW). Además de desarrollar un sistema QbE-STD que sirva como punto de partida para futuras vías de trabajo del grupo AUDIAS1, se han incluido distintas técnicas y aportaciones con el objetivo de intentar mejorar los resultados obtenidos. Entre estas técnicas se encuentra la selección de unidades fonéticas o la fusión de idiomas. Para el desarrollo de la solución y la realización de las pruebas se han utilizado los audios pertenecientes a las evaluaciones Albayzin 2016 y 2018 Search on Speech. Los resultados obtenidos se han podido contrastar con otros sistemas publicados, ya que para el cálculo de la precisión se ha empleado un procedimiento de evaluación oficial propuesto por el instituto de tecnología NIST y ampliamente utilizado. Los valores de precisión alcanzados demuestran que mediante el sistema básico se obtienen unos resultados competitivos y semejantes a los de otras implementaciones de este tipo.In order to extract information and recognize key words in the audio files belonging to media and Internet, QbE-STD (Query-by-Example Spoken Term Detection) systems are developed. QbE-STD systems have as purpose, on the one hand, to search for an example of an object or part of it in another object (QbE), and on the other, to find words or sequences of them in audio files (STD). In this Master Thesis, a language-independent QbE-STD system has been developed, whose input or query is based on spoken terms, which allows an user to perform a search in an audio repository by saying the search term with his/her own voice. As a technique of speech representation, phonetic posteriorgrams have been used, obtained through the phonetic decoders developed by the Brno University of Technology (BUT). The Subsequence Dynamic Time Warping (S-DTW) algorithm has been used to detect the search terms in the audio repositories. In addition to developing a QbE-STD system that will be used as a first point for future investigation of AUDIAS2 group, different techniques and contributions have been included in order to try to improve the achieved results. Among these techniques, the phonetic units selection or the languages fusion have been implemented. In the development and test phases, the audios belonging to the Albayzin 2016 and 2018 Search on Speech evaluation have been used. The achieved results have been compared with other published systems, because of the use of an official evaluation procedure proposed by NIST technology has been implemented to obtain accuracy. The precision values obtained show that competitive results have been achieved through the basic system, and these are similar to those of other implementations of this type

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019