The TransLectures-UPV Toolkit

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_28Over the past few years, online multimedia educational repositories have increased in number and popularity. The main aim of the transLectures project is to develop cost-effective solutions for producing accurate transcriptions and translations for large video lecture repositories, such as VideoLectures.NET or the Universitat Politècnica de València s repository, poliMedia. In this paper, we present the transLectures-UPV toolkit (TLK), which has been specifically designed to meet the requirements of the transLectures project, but can also be used as a conventional ASR toolkit. The main features of the current release include HMM training and decoding with speaker adaptation techniques (fCMLLR). TLK has been tested on the VideoLectures.NET and poliMedia repositories, yielding very competitive results. TLK has been released under the permissive open source Apache License v2.0 and can be directly downloaded from the transLectures website.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and InnovationFramework Programme (CIP) under grant agreement no 621030 (EMMA), andthe Spanish MINECO Active2Trans (TIN2012-31723) research project.Del Agua Teba, MA.; Giménez Pastor, A.; Serrano Martinez Santos, N.; Andrés Ferrer, J.; Civera Saiz, J.; Sanchis Navarro, JA.; Juan Císcar, A. (2014). The TransLectures-UPV Toolkit. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. Springer International Publishing. 269-278. https://doi.org/10.1007/978-3-319-13623-3_28S269278Final report on massive adaptation (M36). To be delivered on October 2014 (2014)First report on massive adaptation (M12), https://www.translectures.eu/wp-content/uploads/2013/05/transLectures-D3.1.1-18Nov2012.pdfOpencast Matterhorn, http://opencast.org/matterhorn/sclite - Score speech recognition system output, http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htmSecond report on massive adaptation (M24), https://www.translectures.eu//wp-content/uploads/2014/01/transLectures-D3.1.2-15Nov2013.pdfTLK: The transLectures-UPV Toolkit, https://www.translectures.eu/tlk/Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains. The Annals of Mathematical Statistics 41(1), 164–171 (1970)Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)Digalakis, V., Rtischev, D., Neumeyer, L., Sa, E.: Speaker Adaptation Using Constrained Estimation of Gaussian Mixtures. IEEE Transactions on Speech and Audio Processing 3, 357–366 (1995)Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: Proc. of ICASSP (2013)Munteanu, C., Baecker, R., Penn, G., Toms, E., James, D.: The Effect of Speech Recognition Accuracy Rates on the Usefulness and Usability of Webcast Archives. In: Proc. of CHI, pp. 493–502 (2006)Ney, H., Ortmanns, S.: Progress in dynamic programming search for LVCSR. Proceedings of the IEEE 88(8), 1224–1240 (2000)Ortmanns, S., Ney, H., Eiden, A.: Language-model look-ahead for large vocabulary speech recognition. In: Proc. of ICSLP, vol. 4, pp. 2095–2098 (1996)Ortmanns, S., Ney, H., Aubert, X.: A word graph algorithm for large vocabulary continuous speech recognition. Computer Speech and Language 11(1), 43–72 (1997)Povey, D., et al.: The Kaldi Speech Recognition Toolkit. In: Proc. of ASRU (2011)Rumelhart, D., Hintont, G., Williams, R.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)Rybach, D., et al.: The RWTH Aachen University Open Source Speech Recognition System. In: Proc. Interspeech, pp. 2111–2114 (2009)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13(2), 260–269 (1967)Young, S., et al.: The HTK Book. Cambridge University Engineering Department (1995)Young, S.J., Odell, J.J., Woodland, P.C.: Tree-based state tying for high accuracy acoustic modelling. In: Proc. of HLT, pp. 307–312 (1994

CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES

Tesis por compendio[ES] Durante los últimos años, los repositorios multimedia en línea se han convertido en fuentes clave de conocimiento gracias al auge de Internet, especialmente en el área de la educación. Instituciones educativas de todo el mundo han dedicado muchos recursos en la búsqueda de nuevos métodos de enseñanza, tanto para mejorar la asimilación de nuevos conocimientos, como para poder llegar a una audiencia más amplia. Como resultado, hoy en día disponemos de diferentes repositorios con clases grabadas que siven como herramientas complementarias en la enseñanza, o incluso pueden asentar una nueva base en la enseñanza a distancia. Sin embargo, deben cumplir con una serie de requisitos para que la experiencia sea totalmente satisfactoria y es aquí donde la transcripción de los materiales juega un papel fundamental. La transcripción posibilita una búsqueda precisa de los materiales en los que el alumno está interesado, se abre la puerta a la traducción automática, a funciones de recomendación, a la generación de resumenes de las charlas y además, el poder hacer llegar el contenido a personas con discapacidades auditivas. No obstante, la generación de estas transcripciones puede resultar muy costosa. Con todo esto en mente, la presente tesis tiene como objetivo proporcionar nuevas herramientas y técnicas que faciliten la transcripción de estos repositorios. En particular, abordamos el desarrollo de un conjunto de herramientas de reconocimiento de automático del habla, con énfasis en las técnicas de aprendizaje profundo que contribuyen a proporcionar transcripciones precisas en casos de estudio reales. Además, se presentan diferentes participaciones en competiciones internacionales donde se demuestra la competitividad del software comparada con otras soluciones. Por otra parte, en aras de mejorar los sistemas de reconocimiento, se propone una nueva técnica de adaptación de estos sistemas al interlocutor basada en el uso Medidas de Confianza. Esto además motivó el desarrollo de técnicas para la mejora en la estimación de este tipo de medidas por medio de Redes Neuronales Recurrentes. Todas las contribuciones presentadas se han probado en diferentes repositorios educativos. De hecho, el toolkit transLectures-UPV es parte de un conjunto de herramientas que sirve para generar transcripciones de clases en diferentes universidades e instituciones españolas y europeas.[CA] Durant els últims anys, els repositoris multimèdia en línia s'han convertit en fonts clau de coneixement gràcies a l'expansió d'Internet, especialment en l'àrea de l'educació. Institucions educatives de tot el món han dedicat molts recursos en la recerca de nous mètodes d'ensenyament, tant per millorar l'assimilació de nous coneixements, com per poder arribar a una audiència més àmplia. Com a resultat, avui dia disposem de diferents repositoris amb classes gravades que serveixen com a eines complementàries en l'ensenyament, o fins i tot poden assentar una nova base a l'ensenyament a distància. No obstant això, han de complir amb una sèrie de requisits perquè la experiència siga totalment satisfactòria i és ací on la transcripció dels materials juga un paper fonamental. La transcripció possibilita una recerca precisa dels materials en els quals l'alumne està interessat, s'obri la porta a la traducció automàtica, a funcions de recomanació, a la generació de resums de les xerrades i el poder fer arribar el contingut a persones amb discapacitats auditives. No obstant, la generació d'aquestes transcripcions pot resultar molt costosa. Amb això en ment, la present tesi té com a objectiu proporcionar noves eines i tècniques que faciliten la transcripció d'aquests repositoris. En particular, abordem el desenvolupament d'un conjunt d'eines de reconeixement automàtic de la parla, amb èmfasi en les tècniques d'aprenentatge profund que contribueixen a proporcionar transcripcions precises en casos d'estudi reals. A més, es presenten diferents participacions en competicions internacionals on es demostra la competitivitat del programari comparada amb altres solucions. D'altra banda, per tal de millorar els sistemes de reconeixement, es proposa una nova tècnica d'adaptació d'aquests sistemes a l'interlocutor basada en l'ús de Mesures de Confiança. A més, això va motivar el desenvolupament de tècniques per a la millora en l'estimació d'aquest tipus de mesures per mitjà de Xarxes Neuronals Recurrents. Totes les contribucions presentades s'han provat en diferents repositoris educatius. De fet, el toolkit transLectures-UPV és part d'un conjunt d'eines que serveix per generar transcripcions de classes en diferents universitats i institucions espanyoles i europees.[EN] During the last years, on-line multimedia repositories have become key knowledge assets thanks to the rise of Internet and especially in the area of education. Educational institutions around the world have devoted big efforts to explore different teaching methods, to improve the transmission of knowledge and to reach a wider audience. As a result, online video lecture repositories are now available and serve as complementary tools that can boost the learning experience to better assimilate new concepts. In order to guarantee the success of these repositories the transcription of each lecture plays a very important role because it constitutes the first step towards the availability of many other features. This transcription allows the searchability of learning materials, enables the translation into another languages, provides recommendation functions, gives the possibility to provide content summaries, guarantees the access to people with hearing disabilities, etc. However, the transcription of these videos is expensive in terms of time and human cost. To this purpose, this thesis aims at providing new tools and techniques that ease the transcription of these repositories. In particular, we address the development of a complete Automatic Speech Recognition Toolkit with an special focus on the Deep Learning techniques that contribute to provide accurate transcriptions in real-world scenarios. This toolkit is tested against many other in different international competitions showing comparable transcription quality. Moreover, a new technique to improve the recognition accuracy has been proposed which makes use of Confidence Measures, and constitutes the spark that motivated the proposal of new Confidence Measures techniques that helped to further improve the transcription quality. To this end, a new speaker-adapted confidence measure approach was proposed for models based on Recurrent Neural Networks. The contributions proposed herein have been tested in real-life scenarios in different educational repositories. In fact, the transLectures-UPV toolkit is part of a set of tools for providing video lecture transcriptions in many different Spanish and European universities and institutions.Agua Teba, MÁD. (2019). CONTRIBUTIONS TO EFFICIENT AUTOMATIC TRANSCRIPTION OF VIDEO LECTURES [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/130198TESISCompendi

Statistical text-to-speech synthesis of Spanish subtitles

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-13623-3_5Online multimedia repositories are growing rapidly. However, language barriers are often difficult to overcome for many of the current and potential users. In this paper we describe a TTS Spanish sys- tem and we apply it to the synthesis of transcribed and translated video lectures. A statistical parametric speech synthesis system, in which the acoustic mapping is performed with either HMM-based or DNN-based acoustic models, has been developed. To the best of our knowledge, this is the first time that a DNN-based TTS system has been implemented for the synthesis of Spanish. A comparative objective evaluation between both models has been carried out. Our results show that DNN-based systems can reconstruct speech waveforms more accurately.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755 (transLectures) and ICT Policy Support Programme (ICT PSP/2007-2013) as part of the Competitiveness and Innovation Framework Programme (CIP) under grant agreement no 621030 (EMMA), and the Spanish MINECO Active2Trans (TIN2012-31723) research project.Piqueras Gozalbes, SR.; Del Agua Teba, MA.; Giménez Pastor, A.; Civera Saiz, J.; Juan Císcar, A. (2014). Statistical text-to-speech synthesis of Spanish subtitles. En Advances in Speech and Language Technologies for Iberian Languages: Second International Conference, IberSPEECH 2014, Las Palmas de Gran Canaria, Spain, November 19-21, 2014. Proceedings. Springer International Publishing. 40-48. https://doi.org/10.1007/978-3-319-13623-3_5S4048Ahocoder, http://aholab.ehu.es/ahocoderCoursera, http://www.coursera.orgHMM-Based Speech Synthesis System (HTS), http://hts.sp.nitech.ac.jpKhan Academy, http://www.khanacademy.orgAxelrod, A., He, X., Gao, J.: Domain adaptation via pseudo in-domain data selection. In: Proc. of EMNLP, pp. 355–362 (2011)Bottou, L.: Stochastic gradient learning in neural networks. In: Proceedings of Neuro-Nîmes 1991. EC2, Nimes, France (1991)Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(1), 30–42 (2012)Erro, D., Sainz, I., Navas, E., Hernaez, I.: Harmonics plus noise model based vocoder for statistical parametric speech synthesis. IEEE Journal of Selected Topics in Signal Processing 8(2), 184–194 (2014)Fan, Y., Qian, Y., Xie, F., Soong, F.: TTS synthesis with bidirectional LSTM based recurrent neural networks. In: Proc. of Interspeech (submitted 2014)Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29(6), 82–97 (2012)Hunt, A.J., Black, A.W.: Unit selection in a concatenative speech synthesis system using a large speech database. In: Proc. of ICASSP, vol. 1, pp. 373–376 (1996)King, S.: Measuring a decade of progress in text-to-speech. Loquens 1(1), e006 (2014)Koehn, P.: Statistical Machine Translation. Cambridge University Press (2010)Kominek, J., Schultz, T., Black, A.W.: Synthesizer voice quality of new languages calibrated with mean mel cepstral distortion. In: Proc. of SLTU, pp. 63–68 (2008)Lopez, A.: Statistical machine translation. ACM Computing Surveys 40(3), 8:1–8:49 (2008)poliMedia: The polimedia video-lecture repository (2007), http://media.upv.esSainz, I., Erro, D., Navas, E., Hernáez, I., Sánchez, J., Saratxaga, I.: Aholab speech synthesizer for albayzin 2012 speech synthesis evaluation. In: Proc. of IberSPEECH, pp. 645–652 (2012)Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent dnn for conversational speech transcription. In: Proc. of ASRU, pp. 24–29 (2011)Shinoda, K., Watanabe, T.: MDL-based context-dependent subword modeling for speech recognition. Journal of the Acoustical Society of Japan 21(2), 79–86 (2000)Silvestre-Cerdà, J.A., et al.: Translectures. In: Proc. of IberSPEECH, pp. 345–351 (2012)TED Ideas worth spreading, http://www.ted.comThe transLectures-UPV Team.: The transLectures-UPV toolkit (TLK), http://translectures.eu/tlkToda, T., Black, A.W., Tokuda, K.: Mapping from articulatory movements to vocal tract spectrum with Gaussian mixture model for articulatory speech synthesis. In: Proc. of ISCA Speech Synthesis Workshop (2004)Tokuda, K., Kobayashi, T., Imai, S.: Speech parameter generation from hmm using dynamic features. In: Proc. of ICASSP, vol. 1, pp. 660–663 (1995)Tokuda, K., Masuko, T., Miyazaki, N., Kobayashi, T.: Multi-space probability distribution HMM. IEICE Transactions on Information and Systems 85(3), 455–464 (2002)transLectures: D3.1.2: Second report on massive adaptation, http://www.translectures.eu/wp-content/uploads/2014/01/transLectures-D3.1.2-15Nov2013.pdfTurró, C., Ferrando, M., Busquets, J., Cañero, A.: Polimedia: a system for successful video e-learning. In: Proc. of EUNIS (2009)Videolectures.NET: Exchange ideas and share knowledge, http://www.videolectures.netWu, Y.J., King, S., Tokuda, K.: Cross-lingual speaker adaptation for HMM-based speech synthesis. In: Proc. of ISCSLP, pp. 1–4 (2008)Yamagishi, J.: An introduction to HMM-based speech synthesis. Tech. rep. Centre for Speech Technology Research (2006), https://wiki.inf.ed.ac.uk/twiki/pub/CSTR/TrajectoryModelling/HTS-Introduction.pdfYoshimura, T., Tokuda, K., Masuko, T., Kobayashi, T., Kitamura, T.: Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis. In: Proc. of Eurospeech, pp. 2347–2350 (1999)Zen, H., Senior, A.: Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis. In: Proc. of ICASSP, pp. 3872–3876 (2014)Zen, H., Senior, A., Schuster, M.: Statistical parametric speech synthesis using deep neural networks. In: Proc. of ICASSP, pp. 7962–7966 (2013)Zen, H., Tokuda, K., Black, A.W.: Statistical parametric speech synthesis. Speech Communication 51(11), 1039–1064 (2009

Deep Maxout Networks applied to Noise-Robust Speech Recognition

Proceedings of: IberSPEECH 2014 "VIII Jornadas en Tecnologías del Habla" and "IV Iberian SLTech Workshop". Las Palmas de Gran Canaria, Spain, November 19-21, 2014.Deep Neural Networks (DNN) have become very popular for acoustic modeling due to the improvements found over traditional Gaussian Mixture Models (GMM). However, not many works have addressed the robustness of these systems under noisy conditions. Recently, the machine learning community has proposed new methods to improve the accuracy of DNNs by using techniques such as dropout and maxout. In this paper, we investigate Deep Maxout Networks (DMN) for acoustic modeling in a noisy automatic speech recognition environment. Experiments show that DMNs improve substantially the recognition accuracy over DNNs and other traditional techniques in both clean and noisy conditions on the TIMIT dataset.This contribution has been supported by an Airbus Defense and Space Grant (Open Innovation - SAVIER) and Spanish Government-CICYT project 2011-26807/TEC.Publicad

O provérbio como estímulo num terapeuta virtual

Os provérbios são elementos úteis no diagnóstico e terapia de certas patologias da linguagem, nomeadamente as que resultam de trauma, já que estão associados a estruturas da memória que são afetadas diferencialmente, mesmo quando a capacidade de falar é diminuída. Por esta razão, os provérbios têm vindo a ser utilizados como auxiliar de diagnóstico e de terapia, na medida em que constituem uma forma de exercício das estruturas cognitivas a partir da memória de longa duração. Apesar da sua ubíqua utilização por parte de terapeutas, a seleção dos provérbios para estímulo numa terapia ou num diagnóstico pode apresentar dificuldades, uma vez que não é fácil determinar se o não reconhecimento de um dado provérbio pelo paciente está associado a quadros patológicos ou ao mero desconhecimento do mesmo. Importa, por isso, que os provérbios e variantes a utilizar para estímulo sejam os de uso mais frequente e que possam ser reconhecidos pela generalidade dos falantes. Nesta comunicação, apresentamos a metodologia seguida na construção de um módulo de exercícios de diferentes tipos envolvendo provérbios a integrar num Terapeuta Virtual para o tratamento da Afasia (VITHEA).Universidade do Algarveinfo:eu-repo/semantics/acceptedVersio

Assessing Lexical-Semantic Regularities in Portuguese Word Embeddings

Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered by some analogy test sets for English. Briefly, this paper aims to study how well pretrained Portuguese word embeddings capture such relations. For this purpose, we created a new test, dubbed TALES, with an exclusive focus on Portuguese lexical-semantic relations, acquired from lexical resources. With TALES, we analyse the performance of methods previously used for solving analogies, on different models of Portuguese word embeddings. Accuracies were clearly below the state of the art in analogies of other kinds, which shows that TALES is a challenging test, mainly due to the nature of lexical-semantic relations, i.e., there are many instances sharing the same argument, thus allowing for several correct answers, sometimes too many to be all included in the dataset. We further inspect the results of the best performing combination of method and model to find that some acceptable answers had been considered incorrect. This was mainly due to the lack of coverage by the source lexical resources and suggests that word embeddings may be a useful source of information for enriching those resources, something we also discuss

Let's play with proverbs? NLP tools and resources for iCALL applications around proverbs for PFL

Proverbs are an important form of cultural expression of a society and are related to various areas of knowledge and human experience (González Rey, 2002). While linguistic elements in widespread use, proverbs are very rich structures both from a cultural and from a linguistic point of view and can therefore contribute significantly to the teaching of languages, both native and foreign (Council of Europe, 2001). However, though there are extensive collections of Portuguese proverbs with tens of thousands of forms and its variants (Reis, in preparation), its automatic identification in texts is quite difficult, given its formal variation, both lexical and syntactic (Chacoto, 1994). Nevertheless, using real examples, where proverbs are used in a natural or spontaneous discourse context, is a more natural way to learn and teach the complex conditions and communicative situations that determine the use and meaning of these expressions. On the other hand, frequency indices associated with proverbs and its variants would allow one to select the most common expressions. These are precisely the most interesting forms from the point of view of their teaching/learning and could serve as a basis for the construction of educational games, particularly for learning Portuguese autonomously as a foreign language (PFL) assisted by computer. To make this possible, it is necessary, first of all, be able to recognize the occurrence of proverbs in the texts (Rassi et al. 2014), including the instances where these expressions are presented in a truncated or creatively modified form, for example, to better suit the communicative situation or to produce new and more expressive meanings. In this paper, we present an on-going project, which aims at automatic identification of proverbs in texts. In this interdisciplinary study, we combine natural language processing tools with questionnaires construction techniques for teaching purposes (Hoshino and Nakagawa 2005, Correia et al. 2010). This is illustrated here with different sets of formats that can be built based on the knowledge of the form and variation of proverbs, as well as their frequency in corpora.info:eu-repo/semantics/publishedVersio

Evaluation of innovative computer-assisted transcription and translation strategies for video lecture repositories

Nowadays, the technology enhanced learning area has experienced a strong growth with many new learning approaches like blended learning, flip teaching, massive open online courses, and open educational resources to complement face-to-face lectures. Specifically, video lectures are fast becoming an everyday educational resource in higher education for all of these new learning approaches, and they are being incorporated into existing university curricula around the world. Transcriptions and translations can improve the utility of these audiovisual assets, but rarely are present due to a lack of cost-effective solutions to do so. Lecture searchability, accessibility to people with impairments, translatability for foreign students, plagiarism detection, content recommendation, note-taking, and discovery of content-related videos are examples of advantages of the presence of transcriptions. For this reason, the aim of this thesis is to test in real-life case studies ways to obtain multilingual captions for video lectures in a cost-effective way by using state-of-the-art automatic speech recognition and machine translation techniques. Also, we explore interaction protocols to review these automatic transcriptions and translations, because unfortunately automatic subtitles are not error-free. In addition, we take a step further into multilingualism by extending our findings and evaluation to several languages. Finally, the outcomes of this thesis have been applied to thousands of video lectures in European universities and institutions.Hoy en día, el área del aprendizaje mejorado por la tecnología ha experimentado un fuerte crecimiento con muchos nuevos enfoques de aprendizaje como el aprendizaje combinado, la clase inversa, los cursos masivos abiertos en línea, y nuevos recursos educativos abiertos para complementar las clases presenciales. En concreto, los videos docentes se están convirtiendo rápidamente en un recurso educativo cotidiano en la educación superior para todos estos nuevos enfoques de aprendizaje, y se están incorporando a los planes de estudios universitarios existentes en todo el mundo. Las transcripciones y las traducciones pueden mejorar la utilidad de estos recursos audiovisuales, pero rara vez están presentes debido a la falta de soluciones rentables para hacerlo. La búsqueda de y en los videos, la accesibilidad a personas con impedimentos, la traducción para estudiantes extranjeros, la detección de plagios, la recomendación de contenido, la toma de notas y el descubrimiento de videos relacionados son ejemplos de las ventajas de la presencia de transcripciones. Por esta razón, el objetivo de esta tesis es probar en casos de estudio de la vida real las formas de obtener subtítulos multilingües para videos docentes de una manera rentable, mediante el uso de técnicas avanzadas de reconocimiento automático de voz y de traducción automática. Además, exploramos diferentes modelos de interacción para revisar estas transcripciones y traducciones automáticas, pues desafortunadamente los subtítulos automáticos no están libres de errores. Además, damos un paso más en el multilingüismo extendiendo nuestros hallazgos y evaluaciones a muchos idiomas. Por último, destacar que los resultados de esta tesis se han aplicado a miles de vídeos docentes en universidades e instituciones europeas.Hui en dia, l'àrea d'aprenentatge millorat per la tecnologia ha experimentat un fort creixement, amb molts nous enfocaments d'aprenentatge com l'aprenentatge combinat, la classe inversa, els cursos massius oberts en línia i nous recursos educatius oberts per tal de complementar les classes presencials. En concret, els vídeos docents s'estan convertint ràpidament en un recurs educatiu quotidià en l'educació superior per a tots aquests nous enfocaments d'aprenentatge i estan incorporant-se als plans d'estudi universitari existents arreu del món. Les transcripcions i les traduccions poden millorar la utilitat d'aquests recursos audiovisuals, però rara vegada estan presents a causa de la falta de solucions rendibles per fer-ho. La cerca de i als vídeos, l'accessibilitat a persones amb impediments, la traducció per estudiants estrangers, la detecció de plagi, la recomanació de contingut, la presa de notes i el descobriment de vídeos relacionats són un exemple dels avantatges de la presència de transcripcions. Per aquesta raó, l'objectiu d'aquesta tesi és provar en casos d'estudi de la vida real les formes d'obtenir subtítols multilingües per a vídeos docents d'una manera rendible, mitjançant l'ús de tècniques avançades de reconeixement automàtic de veu i de traducció automàtica. A més a més, s'exploren diferents models d'interacció per a revisar aquestes transcripcions i traduccions automàtiques, puix malauradament els subtítols automàtics no estan lliures d'errades. A més, es fa un pas més en el multilingüisme estenent els nostres descobriments i avaluacions a molts idiomes. Per últim, destacar que els resultats d'aquesta tesi s'han aplicat a milers de vídeos docents en universitats i institucions europees.Valor Miró, JD. (2017). Evaluation of innovative computer-assisted transcription and translation strategies for video lecture repositories [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90496TESI

Confidence Measures for Automatic and Interactive Speech Recognition

[EN] This thesis work contributes to the field of the {Automatic Speech Recognition} (ASR). And particularly to the {Interactive Speech Transcription} and {Confidence Measures} (CM) for ASR. The main goals of this thesis work can be summarised as follows: 1. To design IST methods and tools to tackle the problem of improving automatically generated transcripts. 2. To assess the designed IST methods and tools on real-life tasks of transcription in large educational repositories of video lectures. 3. To improve the reliability of the IST by improving the underlying (CM). Abstracts: The {Automatic Speech Recognition} (ASR) is a crucial task in a broad range of important applications which could not accomplished by means of manual transcription. The ASR can provide cost-effective transcripts in scenarios of increasing social impact such as the {Massive Open Online Courses} (MOOC), for which the availability of accurate enough is crucial even if they are not flawless. The transcripts enable search-ability, summarisation, recommendation, translation; they make the contents accessible to non-native speakers and users with impairments, etc. The usefulness is such that students improve their academic performance when learning from subtitled video lectures even when transcript is not perfect. Unfortunately, the current ASR technology is still far from the necessary accuracy. The imperfect transcripts resulting from ASR can be manually supervised and corrected, but the effort can be even higher than manual transcription. For the purpose of alleviating this issue, a novel {Interactive Transcription of Speech} (IST) system is presented in this thesis. This IST succeeded in reducing the effort if a small quantity of errors can be allowed; and also in improving the underlying ASR models in a cost-effective way. In other to adequate the proposed framework into real-life MOOCs, another intelligent interaction methods involving limited user effort were investigated. And also, it was introduced a new method which benefit from the user interactions to improve automatically the unsupervised parts ({Constrained Search} for ASR). The conducted research was deployed into a web-based IST platform with which it was possible to produce a massive number of semi-supervised lectures from two different well-known repositories, videoLectures.net and poliMedia. Finally, the performance of the IST and ASR systems can be easily increased by improving the computation of the {Confidence Measure} (CM) of transcribed words. As so, two contributions were developed: a new particular {Logistic Regresion} (LR) model; and the speaker adaption of the CM for cases in which it is possible, such with MOOCs.[ES] Este trabajo contribuye en el campo del {reconocimiento automático del habla} (RAH). Y en especial, en el de la {transcripción interactiva del habla} (TIH) y el de las {medidas de confianza} (MC) para RAH. Los objetivos principales son los siguientes: 1. Diseño de métodos y herramientas TIH para mejorar las transcripciones automáticas. 2. Evaluar los métodos y herramientas TIH empleando tareas de transcripción realistas extraídas de grandes repositorios de vídeos educacionales. 3. Mejorar la fiabilidad del TIH mediante la mejora de las MC. Resumen: El {reconocimiento automático del habla} (RAH) es una tarea crucial en una amplia gama de aplicaciones importantes que no podrían realizarse mediante transcripción manual. El RAH puede proporcionar transcripciones rentables en escenarios de creciente impacto social como el de los {cursos abiertos en linea masivos} (MOOC), para el que la disponibilidad de transcripciones es crucial, incluso cuando no son completamente perfectas. Las transcripciones permiten la automatización de procesos como buscar, resumir, recomendar, traducir; hacen que los contenidos sean más accesibles para hablantes no nativos y usuarios con discapacidades, etc. Incluso se ha comprobado que mejora el rendimiento de los estudiantes que aprenden de videos con subtítulos incluso cuando estos no son completamente perfectos. Desafortunadamente, la tecnología RAH actual aún está lejos de la precisión necesaria. Las transcripciones imperfectas resultantes del RAH pueden ser supervisadas y corregidas manualmente, pero el esfuerzo puede ser incluso superior al de la transcripción manual. Con el fin de aliviar este problema, esta tesis presenta un novedoso sistema de {transcripción interactiva del habla} (TIH). Este método TIH consigue reducir el esfuerzo de semi-supervisión siempre que sea aceptable una pequeña cantidad de errores; además mejora a la par los modelos RAH subyacentes. Con objeto de transportar el marco propuesto para MOOCs, también se investigaron otros métodos de interacción inteligentes que involucran esfuerzo limitado por parte del usuario. Además, se introdujo un nuevo método que aprovecha las interacciones para mejorar aún más las partes no supervisadas (ASR con {búsqueda restringida}). La investigación en TIH llevada a cabo se desplegó en una plataforma web con el que fue posible producir un número masivo de transcripciones de videos de dos conocidos repositorios, videoLectures.net y poliMedia. Por último, el rendimiento de la TIH y los sistemas de RAH se puede aumentar directamente mediante la mejora de la estimación de la {medida de confianza} (MC) de las palabras transcritas. Por este motivo se desarrollaron dos contribuciones: un nuevo modelo discriminativo {logístico} (LR); y la adaptación al locutor de la MC para los casos en que es posible, como por ejemplo en MOOCs.[CA] Aquest treball hi contribueix al camp del {reconeixment automàtic de la parla} (RAP). I en especial, al de la {transcripció interactiva de la parla} i el de {mesures de confiança} (MC) per a RAP. Els objectius principals són els següents: 1. Dissenyar mètodes i eines per a TIP per tal de millorar les transcripcions automàtiques. 2. Avaluar els mètodes i eines TIP per a tasques de transcripció realistes extretes de grans repositoris de vídeos educacionals. 3. Millorar la fiabilitat del TIP, mitjançant la millora de les MC. Resum: El {reconeixment automàtic de la parla} (RAP) és una tasca crucial per una àmplia gamma d'aplicacions importants que no es poden dur a terme per mitjà de la transcripció manual. El RAP pot proporcionar transcripcions en escenaris de creixent impacte social com els {cursos online oberts massius} (MOOC). Les transcripcions permeten automatitzar tasques com ara cercar, resumir, recomanar, traduir; a més a més, fa accessibles els continguts als parlants no nadius i els usuaris amb discapacitat, etc. Fins i tot, pot millorar el rendiment acadèmic de estudiants que aprenen de xerrades amb subtítols, encara que aquests subtítols no siguen perfectes. Malauradament, la tecnologia RAP actual encara està lluny de la precisió necessària. Les transcripcions imperfectes resultants de RAP poden ser supervisades i corregides manualment, però aquest l'esforç pot acabar sent superior a la transcripció manual. Per tal de resoldre aquest problema, en aquest treball es presenta un sistema nou per a {transcripció interactiva de la parla} (TIP). Aquest sistema TIP va ser reeixit en la reducció de l'esforç per quan es pot permetre una certa quantitat d'errors; així com també en en la millora dels models RAP subjacents. Per tal d'adequar el marc proposat per a MOOCs, també es van investigar altres mètodes d'interacció intel·ligents amb esforç d''usuari limitat. A més a més, es va introduir un nou mètode que aprofita les interaccions per tal de millorar encara més les parts no supervisades (RAP amb {cerca restringida}). La investigació en TIP duta a terme es va desplegar en una plataforma web amb la qual va ser possible produir un nombre massiu de transcripcions semi-supervisades de xerrades de repositoris ben coneguts, videoLectures.net i poliMedia. Finalment, el rendiment de la TIP i els sistemes de RAP es pot augmentar directament mitjançant la millora de l'estimació de la {Confiança Mesura} (MC) de les paraules transcrites. Per tant, es van desenvolupar dues contribucions: un nou model discriminatiu logístic (LR); i l'adaptació al locutor de la MC per casos en que és possible, per exemple amb MOOCs.Sánchez Cortina, I. (2016). Confidence Measures for Automatic and Interactive Speech Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61473TESI