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

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

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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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-

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0063-8Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).This work has been partly supported by project CMC-V2 (TEC2012-37585-C02-01) from the Spanish Ministry of Economy and Competitiveness. This research was also funded by the European Regional Development Fund, the Galician Regional Government (GRC2014/024, “Consolidation of Research Units: AtlantTIC Project” CN2012/160)

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

Intelligent system for spoken term detection using the belief combination

Spoken Term Detection (STD) can be considered as a sub-part of the automatic speech recognition which aims to extract the partial information from speech signals in the form of query utterances. A variety of STD techniques available in the literature employ a single source of evidence for the query utterance match/mismatch determination. In this manuscript, we develop an acoustic signal processing based approach for STD that incorporates a number of techniques for silence removal, dynamic noise filtration, and evidence combination using Dempster-Shafer Theory (DST). A ‘spectral-temporal features based voiced segment detection’ and ‘energy and zero cross rate based unvoiced segment detection’ are built to remove the silence segments in the speech signal. Comprehensive experiments have been performed on large speech datasets and consequently satisfactory results have been achieved with the proposed approach. Our approach improves the existing speaker dependent STD approaches, specifically the reliability of query utterance spotting by combining the evidences from multiple belief sources

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

IberSPEECH 2020: XI Jornadas en Tecnología 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 Tecnologías del Habla. Universidad de Valladoli

Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models

Tesis por compendio[ES] Durante la última década, los medios de comunicación han experimentado una revolución, alejándose de la televisión convencional hacia las plataformas de contenido bajo demanda. Además, esta revolución no ha cambiado solamente la manera en la que nos entretenemos, si no también la manera en la que aprendemos. En este sentido, las plataformas de contenido educativo bajo demanda también han proliferado para proporcionar recursos educativos de diversos tipos. Estas nuevas vías de distribución de contenido han llegado con nuevos requisitos para mejorar la accesibilidad, en particular las relacionadas con las dificultades de audición y las barreras lingüísticas. Aquí radica la oportunidad para el reconocimiento automático del habla (RAH) para cumplir estos requisitos, proporcionando subtitulado automático de alta calidad. Este subtitulado proporciona una base sólida para reducir esta brecha de accesibilidad, especialmente para contenido en directo o streaming. Estos sistemas de streaming deben trabajar bajo estrictas condiciones de tiempo real, proporcionando la subtitulación tan rápido como sea posible, trabajando con un contexto limitado. Sin embargo, esta limitación puede conllevar una degradación de la calidad cuando se compara con los sistemas para contenido en diferido u offline. Esta tesis propone un sistema de RAH en streaming con baja latencia, con una calidad similar a un sistema offline. Concretamente, este trabajo describe el camino seguido desde el sistema offline híbrido inicial hasta el eficiente sistema final de reconocimiento en streaming. El primer paso es la adaptación del sistema para efectuar una sola iteración de reconocimiento haciendo uso de modelos de lenguaje estado del arte basados en redes neuronales. En los sistemas basados en múltiples iteraciones estos modelos son relegados a una segunda (o posterior) iteración por su gran coste computacional. Tras adaptar el modelo de lenguaje, el modelo acústico basado en redes neuronales también tiene que adaptarse para trabajar con un contexto limitado. La integración y la adaptación de estos modelos es ampliamente descrita en esta tesis, evaluando el sistema RAH resultante, completamente adaptado para streaming, en conjuntos de datos académicos extensamente utilizados y desafiantes tareas basadas en contenidos audiovisuales reales. Como resultado, el sistema proporciona bajas tasas de error con un reducido tiempo de respuesta, comparables al sistema offline.[CA] Durant l'última dècada, els mitjans de comunicació han experimentat una revolució, allunyant-se de la televisió convencional cap a les plataformes de contingut sota demanda. A més a més, aquesta revolució no ha canviat només la manera en la que ens entretenim, si no també la manera en la que aprenem. En aquest sentit, les plataformes de contingut educatiu sota demanda també han proliferat pera proporcionar recursos educatius de diversos tipus. Aquestes noves vies de distribució de contingut han arribat amb nous requisits per a millorar l'accessibilitat, en particular les relacionades amb les dificultats d'audició i les barreres lingüístiques. Aquí radica l'oportunitat per al reconeixement automàtic de la parla (RAH) per a complir aquests requisits, proporcionant subtitulat automàtic d'alta qualitat. Aquest subtitulat proporciona una base sòlida per a reduir aquesta bretxa d'accessibilitat, especialment per a contingut en directe o streaming. Aquests sistemes han de treballar sota estrictes condicions de temps real, proporcionant la subtitulació tan ràpid com sigui possible, treballant en un context limitat. Aquesta limitació, però, pot comportar una degradació de la qualitat quan es compara amb els sistemes per a contingut en diferit o offline. Aquesta tesi proposa un sistema de RAH en streaming amb baixa latència, amb una qualitat similar a un sistema offline. Concretament, aquest treball descriu el camí seguit des del sistema offline híbrid inicial fins l'eficient sistema final de reconeixement en streaming. El primer pas és l'adaptació del sistema per a efectuar una sola iteració de reconeixement fent servir els models de llenguatge de l'estat de l'art basat en xarxes neuronals. En els sistemes basats en múltiples iteracions aquests models son relegades a una segona (o posterior) iteració pel seu gran cost computacional. Un cop el model de llenguatge s'ha adaptat, el model acústic basat en xarxes neuronals també s'ha d'adaptar per a treballar amb un context limitat. La integració i l'adaptació d'aquests models és àmpliament descrita en aquesta tesi, avaluant el sistema RAH resultant, completament adaptat per streaming, en conjunts de dades acadèmiques àmpliament utilitzades i desafiants tasques basades en continguts audiovisuals reals. Com a resultat, el sistema proporciona baixes taxes d'error amb un reduït temps de resposta, comparables al sistema offline.[EN] Over the last decade, the media have experienced a revolution, turning away from the conventional TV in favor of on-demand platforms. In addition, this media revolution not only changed the way entertainment is conceived but also how learning is conducted. Indeed, on-demand educational platforms have also proliferated and are now providing educational resources on diverse topics. These new ways to distribute content have come along with requirements to improve accessibility, particularly related to hearing difficulties and language barriers. Here is the opportunity for automatic speech recognition (ASR) to comply with these requirements by providing high-quality automatic captioning. Automatic captioning provides a sound basis for diminishing the accessibility gap, especially for live or streaming content. To this end, streaming ASR must work under strict real-time conditions, providing captions as fast as possible, and working with limited context. However, this limited context usually leads to a quality degradation as compared to the pre-recorded or offline content. This thesis is aimed at developing low-latency streaming ASR with a quality similar to offline ASR. More precisely, it describes the path followed from an initial hybrid offline system to an efficient streaming-adapted system. The first step is to perform a single recognition pass using a state-of-the-art neural network-based language model. In conventional multi-pass systems, this model is often deferred to the second or later pass due to its computational complexity. As with the language model, the neural-based acoustic model is also properly adapted to work with limited context. The adaptation and integration of these models is thoroughly described and assessed using fully-fledged streaming systems on well-known academic and challenging real-world benchmarks. In brief, it is shown that the proposed adaptation of the language and acoustic models allows the streaming-adapted system to reach the accuracy of the initial offline system with low latency.Jorge Cano, J. (2022). Streaming Automatic Speech Recognition with Hybrid Architectures and Deep Neural Network Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191001Compendi