7 research outputs found
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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Albayzin 2018 Evaluation: The IberSpeech-RTVE Challenge on Speech Technologies for Spanish Broadcast Media
The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Temática en TecnologĂas del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Española (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained
Multimodal Diarization Systems by Training Enrollment Models as Identity Representations
This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system
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
IberSPEECH 2020: XI Jornadas en TecnologĂa del Habla and VII Iberian SLTech
IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de TecnologĂas del Habla. Universidad de Valladoli
Speaker Diarization
DisertaÄŤnĂ práce se zaměřuje na tĂ©ma diarizace Ĺ™eÄŤnĂkĹŻ, coĹľ je Ăşloha zpracovánĂ Ĺ™eÄŤi typicky charakterizovaná otázkou "Kdo kdy mluvĂ?". Práce se takĂ© zabĂ˝vá souvisejĂcĂ Ăşlohou detekce pĹ™ekrĂ˝vajĂcĂ se Ĺ™eÄŤi, která je velmi relevantnĂ pro diarizaci.
Teoretická část práce poskytuje pĹ™ehled existujĂcĂch metod diarizace Ĺ™eÄŤnĂkĹŻ, a to jak tÄ›ch offline, tak online, a pĹ™ibliĹľuje nÄ›kolik problematickĂ˝ch oblastĂ, kterĂ© byly identifikovány v ranĂ© fázi autorÄŤina vĂ˝zkumu. V práci je takĂ© pĹ™edloĹľeno rozsáhlĂ© srovnánĂ existujĂcĂch systĂ©mĹŻ se zaměřenĂm na jejich uvádÄ›nĂ© vĂ˝sledky. Jedna kapitola se takĂ© zaměřuje na tĂ©ma pĹ™ekrĂ˝vajĂcĂ se Ĺ™eÄŤi a na metody jejĂ detekce.
Experimentálnà část práce pĹ™edkládá praktickĂ© vĂ˝stupy, kterĂ˝ch bylo dosaĹľeno. Experimenty s diarizacĂ se zaměřovaly zejmĂ©na na online systĂ©m zaloĹľenĂ˝ na GMM a na i-vektorovĂ˝ systĂ©m, kterĂ˝ mÄ›l offline i online varianty. ZávÄ›reÄŤná sekce experimentĹŻ takĂ© pĹ™ibliĹľuje novÄ› navrĹľenou metodu pro detekci pĹ™ekrĂ˝vajĂcĂ se Ĺ™eÄŤi, která je zaloĹľena na konvoluÄŤnĂ neuronovĂ© sĂti.ObhájenoThe thesis focuses on the topic of speaker diarization, a speech processing task that is commonly characterized as the question "Who speaks when?". It also addresses the related task of overlapping speech detection, which is very relevant for diarization.
The theoretical part of the thesis provides an overview of existing diarization approaches, both offline and online, and discusses some of the problematic areas which were identified in early stages of the author's research. The thesis also includes an extensive comparison of existing diarization systems, with focus on their reported performance. One chapter is also dedicated to the topic of overlapping speech and the methods of its detection.
The experimental part of the thesis then presents the work which has been done on speaker diarization, which was focused mostly on a GMM-based online diarization system and an i-vector based system with both offline and online variants. The final section also details a newly proposed approach for detecting overlapping speech using a convolutional neural network
Advances in Subspace-based Solutions for Diarization in the Broadcast Domain
La motivaciĂłn de esta tesis es la necesidad de soluciones robustas al problema de diarizaciĂłn. Estas tĂ©cnicas de diarizaciĂłn deben proporcionar valor añadido a la creciente cantidad disponible de datos multimedia mediante la precisa discriminaciĂłn de los locutores presentes en la señal de audio. Desafortunadamente, hasta tiempos recientes este tipo de tecnologĂas solamente era viable en condiciones restringidas, quedando por tanto lejos de una soluciĂłn general. Las razones detrás de las limitadas prestaciones de los sistemas de diarizaciĂłn son mĂşltiples. La primera causa a tener en cuenta es la alta complejidad de la producciĂłn de la voz humana, en particular acerca de los procesos fisiolĂłgicos necesarios para incluir las caracterĂsticas discriminativas de locutor en la señal de voz. Esta complejidad hace del proceso inverso, la estimaciĂłn de dichas caracterĂsticas a partir del audio, una tarea ineficiente por medio de las tĂ©cnicas actuales del estado del arte. Consecuentemente, en su lugar deberán tenerse en cuenta aproximaciones. Los esfuerzos en la tarea de modelado han proporcionado modelos cada vez más elaborados, aunque no buscando la explicaciĂłn Ăşltima de naturaleza fisiolĂłgica de la señal de voz. En su lugar estos modelos aprenden relaciones entre la señales acĂşsticas a partir de un gran conjunto de datos de entrenamiento. El desarrollo de modelos aproximados genera a su vez una segunda razĂłn, la variabilidad de dominio. Debido al uso de relaciones aprendidas a partir de un conjunto de entrenamiento concreto, cualquier cambio de dominio que modifique las condiciones acĂşsticas con respecto a los datos de entrenamiento condiciona las relaciones asumidas, pudiendo causar fallos consistentes en los sistemas.Nuestra contribuciĂłn a las tecnologĂas de diarizaciĂłn se ha centrado en el entorno de radiodifusiĂłn. Este dominio es actualmente un entorno todavĂa complejo para los sistemas de diarizaciĂłn donde ninguna simplificaciĂłn de la tarea puede ser tenida en cuenta. Por tanto, se deberá desarrollar un modelado eficiente del audio para extraer la informaciĂłn de locutor y como inferir el etiquetado correspondiente. Además, la presencia de mĂşltiples condiciones acĂşsticas debido a la existencia de diferentes programas y/o gĂ©neros en el domino requiere el desarrollo de tĂ©cnicas capaces de adaptar el conocimiento adquirido en un determinado escenario donde la informaciĂłn está disponible a aquellos entornos donde dicha informaciĂłn es limitada o sencillamente no disponible.Para este propĂłsito el trabajo desarrollado a lo largo de la tesis se ha centrado en tres subtareas: caracterizaciĂłn de locutor, agrupamiento y adaptaciĂłn de modelos. La primera subtarea busca el modelado de un fragmento de audio para obtener representaciones precisas de los locutores involucrados, poniendo de manifiesto sus propiedades discriminativas. En este área se ha llevado a cabo un estudio acerca de las actuales estrategias de modelado, especialmente atendiendo a las limitaciones de las representaciones extraĂdas y poniendo de manifiesto el tipo de errores que pueden generar. Además, se han propuesto alternativas basadas en redes neuronales haciendo uso del conocimiento adquirido. La segunda tarea es el agrupamiento, encargado de desarrollar estrategias que busquen el etiquetado Ăłptimo de los locutores. La investigaciĂłn desarrollada durante esta tesis ha propuesto nuevas estrategias para estimar el mejor reparto de locutores basadas en tĂ©cnicas de subespacios, especialmente PLDA. Finalmente, la tarea de adaptaciĂłn de modelos busca transferir el conocimiento obtenido de un conjunto de entrenamiento a dominios alternativos donde no hay datos para extraerlo. Para este propĂłsito los esfuerzos se han centrado en la extracciĂłn no supervisada de informaciĂłn de locutor del propio audio a diarizar, sinedo posteriormente usada en la adaptaciĂłn de los modelos involucrados.<br /