33 research outputs found
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 investigaciĂłn de Galicia; ED431G/04Universidad del PaĂs Vasco; GIU16/68Ministerio de EconomĂa y Competitividad; TEC2015-68172-C2-1-PMinisterio de Ciencia, InnovaciĂłn y Competitividad; RTI2018-098091-B-I00Xunta de Galicia; ED431G/0
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.; PerdigĂŁo, F.; GarcĂa-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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State of the NASA Aeropropulsion Discipline Input from the Glenn Research Center
PROBLEM: Current power turbines are designed for single operating speed, and performance degrades rapidly as power turbine speed decreases. OBJECTIVES: Demonstrate 50 improvement in efficient operational capability using a Variable Speed Power Turbine concept. (Refer to figure lower left, where the goal is to raise efficiency from the current technology line to the green line which represents the AVSPOT VSPT goal.APPROACH: Conduct RD required to advance the technology readiness level of VSPT technology to TRL 4Partner with DoD and leverage DOD AVSPOT contract to share government cost (5050) of contracted efforts to GE and PW for VSPT TRL 45 demonstration
Neural approaches to spoken content embedding
Comparing spoken segments is a central operation to speech processing.
Traditional approaches in this area have favored frame-level dynamic
programming algorithms, such as dynamic time warping, because they require no
supervision, but they are limited in performance and efficiency. As an
alternative, acoustic word embeddings -- fixed-dimensional vector
representations of variable-length spoken word segments -- have begun to be
considered for such tasks as well. However, the current space of such
discriminative embedding models, training approaches, and their application to
real-world downstream tasks is limited. We start by considering ``single-view"
training losses where the goal is to learn an acoustic word embedding model
that separates same-word and different-word spoken segment pairs. Then, we
consider ``multi-view" contrastive losses. In this setting, acoustic word
embeddings are learned jointly with embeddings of character sequences to
generate acoustically grounded embeddings of written words, or acoustically
grounded word embeddings.
In this thesis, we contribute new discriminative acoustic word embedding
(AWE) and acoustically grounded word embedding (AGWE) approaches based on
recurrent neural networks (RNNs). We improve model training in terms of both
efficiency and performance. We take these developments beyond English to
several low-resource languages and show that multilingual training improves
performance when labeled data is limited. We apply our embedding models, both
monolingual and multilingual, to the downstream tasks of query-by-example
speech search and automatic speech recognition. Finally, we show how our
embedding approaches compare with and complement more recent self-supervised
speech models.Comment: PhD thesi
DetecciĂłn automática de segmentos acĂşsticos e inferencia de unidades lingĂĽĂsticas
[EN] In this project different tasks related to natural language processing are stud-
ied. Specifically, the work is based on spoken language tasks where the
amount of resources is limited or inexistent for the language. These tasks are
“Query-by-Example”, which consists in finding within an audio repository
similar segments to the query; and “Spoken Term Discovery” which consists
in identifying common audio segments, with or without linguistic meaning.
This is a new approach in this kind of tasks, where it is usual to use a great
amount of data in the appropiate language. The tasks presented in this work
are part of some competitions, like MediaEval BenchMark[ES] En este proyecto se estudian diferentes tareas relacionadas con el procesamiento de lenguaje natural. En concreto, el trabajo consiste en tareas basadas en lenguaje hablado
en casos donde la cantidad de recursos es limitada o inexistente para el idioma a tratar. Los problemas que se abordan son Query-by-Example , que consiste en encontrar en un repositorio de audio, segmentos de semejantes al query, que tambiĂ©n es audio; o Spoken Term Discovery que consiste en identificar en un audio segmentos comunes, con o sin significado lingĂĽĂstico. Esto supone un nuevo enfoque en este tipo de tareas, donde es habitual disponer de una gran cantidad de recursos en el idioma correspondiente. Las tareas presentadas en este trabajo forman parte de concursos de evaluaciĂłn, como el MediaEval BenchMark.Laguna Bello, S. (2016). DetecciĂłn automática de segmentos acĂşsticos e inferencia de unidades lingĂĽĂsticas. http://hdl.handle.net/10251/77528TFG
Query-by-Example Keyword Spotting
Tato diplomová práce se zabĂ˝vá modernĂmi pĹ™Ăstupy detekce klĂÄŤovĂ˝ch slov a detekce frázĂ v Ĺ™eÄŤovĂ˝ch datech. V Ăşvodnà části je seznámenĂ s problematikou a teoretickĂ˝ popis metod pro detekci. Následuje popis reprezentace vstupnĂch datovĂ˝ch sad pouĹľitĂ˝ch pĹ™i experimentech a evaluaci. Dále jsou uvedeny metody pro detekci klĂÄŤovĂ˝ch slov definovanĂ˝ch vzorem. NáslednÄ› jsou popsány evaluaÄŤnĂ metody a techniky pouĹľitĂ© pro skĂłrovánĂ. Po provedenĂ experimentĹŻ na datovĂ˝ch sadách a po evaluaci jsou diskutovány vĂ˝sledky. V dalšĂm kroku jsou navrĹľeny a potĂ© implementovány modernĂ postupy vedoucĂ k vylepšenĂ systĂ©mu pro detekci a opÄ›t je provedena evaluace a diskuze dosaĹľenĂ˝ch vĂ˝sledkĹŻ. V závÄ›reÄŤnĂ© části je práce zhodnocena a jsou zde navrĹľeny dalšà smÄ›y vĂ˝voje našeho systĂ©mu. PĹ™Ăloha obsahuje manuál pro pouĹľĂvánĂ implementovanĂ˝ch skriptĹŻ.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.
An Overview of the Layered and Extensible Aircraft Performance System (LEAPS) Development
The Layered and Extensible Aircraft Performance System (LEAPS) is a new sizing and synthesis tool being developed within the Aeronautics Systems Analysis Branch (ASAB) at NASA Langley Research Center. It is a modular, multidisciplinary, multi- fidelity sizing and synthesis tool for modeling advanced aircraft concepts and architectures such as electric/hybrid-electric propulsion, unconventional propulsion airframe integration, and non-traditional mission trajectories. The development of LEAPS is motivated by the lack of existing tools that meet the needs of ASAB. The Flight Optimization System (FLOPS) has been the primary sizing and synthesis tool of ASAB for three decades. However, FLOPS has a number of limitations that make it dicult to use for unconventional aircraft designs. Three high-level goals have been adopted to guide the LEAPS development pro- cess. LEAPS is being developed in Python with an architecture built to enable a exible and extensible analysis capability using the concept of an aircraft object that combines data and analysis models. Five challenge problems for LEAPS have been identi ed to measure progress: analysis of a conventional tube-and-wing aircraft using legacy methods, coupled aeroelastic analysis for weight estimation of a conventional tube-and-wing aircraft, analysis of an advanced hybrid-electric concept, analysis of the X-57 Maxwell distributed electric propulsion aircraft, and optimization of the trajectory of a supersonic vehicle to minimize sonic boom. LEAPS will be a publicly available capability of exceptional quality with modularity and extensibility that makes it a robust tool for design and analysis of current and future unconventional aircraft concepts
Quiet Supersonic Flights 2018 (QSF18) Test: Galveston, Texas Risk Reduction for Future Community Testing with a Low-Boom Flight Demonstration Vehicle
The Quiet Supersonic Flights 2018 (QSF18) Program was designed to develop tools and methods for demonstration of overland supersonic flight with an acceptable sonic boom, and collect a large dataset of responses from a representative sample of the population. Phase 1 provided the basis for a low amplitude sonic boom testing in six different climate regions that will enable international regulatory agencies to draft a noise-based standard for certifying civilian supersonic overland flight. Phase 2 successfully executed a large scale test in Galveston, Texas, developed well documented data sets, calculated dose response relationships, yielded lessons, and identified future risk reduction activities
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