Out-of-vocabulary spoken term detection

Spoken term detection (STD) is a fundamental task for multimedia information retrieval. A major challenge faced by an STD system is the serious performance reduction when detecting out-of-vocabulary (OOV) terms. The difficulties arise not only from the absence of pronunciations for such terms in the system dictionaries, but from intrinsic uncertainty in pronunciations, significant diversity in term properties and a high degree of weakness in acoustic and language modelling. To tackle the OOV issue, we first applied the joint-multigram model to predict pronunciations for OOV terms in a stochastic way. Based on this, we propose a stochastic pronunciation model that considers all possible pronunciations for OOV terms so that the high pronunciation uncertainty is compensated for. Furthermore, to deal with the diversity in term properties, we propose a termdependent discriminative decision strategy, which employs discriminative models to integrate multiple informative factors and confidence measures into a classification probability, which gives rise to minimum decision cost. In addition, to address the weakness in acoustic and language modelling, we propose a direct posterior confidence measure which replaces the generative models with a discriminative model, such as a multi-layer perceptron (MLP), to obtain a robust confidence for OOV term detection. With these novel techniques, the STD performance on OOV terms was improved substantially and significantly in our experiments set on meeting speech data

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

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

Conversational Arabic Automatic Speech Recognition

Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects and are considered generally to be mother-tongues in those regions. CA has limited textual resource because it exists only as a spoken language and without a standardised written form. Normally the modern standard Arabic (MSA) writing convention is employed that has limitations in phonetically representing CA. Without phonetic dictionaries the pronunciation of CA words is ambiguous, and can only be obtained through word and/or sentence context. Moreover, CA inherits the MSA complex word structure where words can be created from attaching affixes to a word. In automatic speech recognition (ASR), commonly used approaches to model acoustic, pronunciation and word variability are language independent. However, one can observe significant differences in performance between English and CA, with the latter yielding up to three times higher error rates. This thesis investigates the main issues for the under-performance of CA ASR systems. The work focuses on two directions: first, the impact of limited lexical coverage, and insufficient training data for written CA on language modelling is investigated; second, obtaining better models for the acoustics and pronunciations by learning to transfer between written and spoken forms. Several original contributions result from each direction. Using data-driven classes from decomposed text are shown to reduce out-of-vocabulary rate. A novel colloquialisation system to import additional data is introduced; automatic diacritisation to restore the missing short vowels was found to yield good performance; and a new acoustic set for describing CA was defined. Using the proposed methods improved the ASR performance in terms of word error rate in a CA conversational telephone speech ASR task

Speech technologies for the audiovisual and multimedia interaction environments

361 p

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

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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