Integrating a State-of-the-Art ASR System into the Opencast Matterhorn Platform

[EN] In this paper we present the integration of a state-of-the-art ASR system into the Opencast Matterhorn platform, a free, open-source platform to support the management of educational audio and video content. The ASR system was trained on a novel large speech corpus, known as poliMedia, that was manually transcribed for the European project transLectures. This novel corpus contains more than 115 hours of transcribed speech that will be available for the research community. Initial results on the poliMedia corpus are also reported to compare the performance of different ASR systems based on the linear interpolation of language models. To this purpose, the in-domain poliMedia corpus was linearly interpolated with an external large-vocabulary dataset, the well-known Google N-Gram corpus. WER figures reported denote the notable improvement over the baseline performance as a result of incorporating the vast amount of data represented by the Google N-Gram corpus.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 287755. Also supported by the Spanish Government (MIPRCV ”Consolider Ingenio 2010” and iTrans2 TIN2009-14511) and the Generalitat Valenciana (Prometeo/2009/014).Valor Miró, JD.; Pérez González De Martos, AM.; Civera Saiz, J.; Juan Císcar, A. (2012). Integrating a State-of-the-Art ASR System into the Opencast Matterhorn Platform. Communications in Computer and Information Science. 328:237-246. https://doi.org/10.1007/978-3-642-35292-8_25S237246328UPVLC, XEROX, JSI-K4A, RWTH, EML, DDS: transLectures: Transcription and Translation of Video Lectures. In: Proc. of EAMT, p. 204 (2012)Zhan, P., Ries, K., Gavalda, M., Gates, D., Lavie, A., Waibel, A.: JANUS-II: towards spontaneous Spanish speech recognition 4, 2285–2288 (1996)Nogueiras, A., Fonollosa, J.A.R., Bonafonte, A., Mariño, J.B.: RAMSES: El sistema de reconocimiento del habla continua y gran vocabulario desarrollado por la UPC. In: VIII Jornadas de I+D en Telecomunicaciones, pp. 399–408 (1998)Huang, X., Alleva, F., Hon, H.W., Hwang, M.Y., Rosenfeld, R.: The SPHINX-II Speech Recognition System: An Overview. Computer, Speech and Language 7, 137–148 (1992)Speech and Language Technology Group. Sumat: An online service for subtitling by machine translation (May 2012), http://www.sumat-project.euBroman, S., Kurimo, M.: Methods for combining language models in speech recognition. In: Proc. of Interspeech, pp. 1317–1320 (2005)Liu, X., Gales, M., Hieronymous, J., Woodland, P.: Use of contexts in language model interpolation and adaptation. In: Proc. of Interspeech (2009)Liu, X., Gales, M., Hieronymous, J., Woodland, P.: Language model combination and adaptation using weighted finite state transducers (2010)Goodman, J.T.: Putting it all together: Language model combination. In: Proc. of ICASSP, pp. 1647–1650 (2000)Lööf, J., Gollan, C., Hahn, S., Heigold, G., Hoffmeister, B., Plahl, C., Rybach, D., Schlüter, R., Ney, H.: The rwth 2007 tc-star evaluation system for european english and spanish. In: Proc. of Interspeech, pp. 2145–2148 (2007)Rybach, D., Gollan, C., Heigold, G., Hoffmeister, B., Lööf, J., Schlüter, R., Ney, H.: The rwth aachen university open source speech recognition system. In: Proc. of Interspeech, pp. 2111–2114 (2009)Stolcke, A.: SRILM - An Extensible Language Modeling Toolkit. In: Proc. of ICSLP (2002)Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331(6014), 176–182Turro, C., Cañero, A., Busquets, J.: Video learning objects creation with polimedia. In: 2010 IEEE International Symposium on Multimedia (ISM), December 13-15, pp. 371–376 (2010)Barras, C., Geoffrois, E., Wu, Z., Liberman, M.: Transcriber: development and use of a tool for assisting speech corpora production. Speech Communication Special Issue on Speech Annotation and Corpus Tools 33(1-2) (2000)Apache. Apache felix (May 2012), http://felix.apache.org/site/index.htmlOsgi alliance. osgi r4 service platform (May 2012), http://www.osgi.org/Main/HomePageSahidullah, M., Saha, G.: Design, analysis and experimental evaluation of block based transformation in MFCC computation for speaker recognition 54(4), 543–565 (2012)Gascó, G., Rocha, M.-A., Sanchis-Trilles, G., Andrés-Ferrer, J., Casacuberta, F.: Does more data always yield better translations? In: Proc. of EACL, pp. 152–161 (2012)Sánchez-Cortina, I., Serrano, N., Sanchis, A., Juan, A.: A prototype for interactive speech transcription balancing error and supervision effort. In: Proc. of IUI, pp. 325–326 (2012

Advanced Speech Communication System for Deaf People

This paper describes the development of an Advanced Speech Communication System for Deaf People and its field evaluation in a real application domain: the renewal of Driver’s License. The system is composed of two modules. The first one is a Spanish into Spanish Sign Language (LSE: Lengua de Signos Española) translation module made up of a speech recognizer, a natural language translator (for converting a word sequence into a sequence of signs), and a 3D avatar animation module (for playing back the signs). The second module is a Spoken Spanish generator from sign writing composed of a visual interface (for specifying a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, a text to speech converter. For language translation, the system integrates three technologies: an example based strategy, a rule based translation method and a statistical translator. This paper also includes a detailed description of the evaluation carried out in the Local Traffic Office in the city of Toledo (Spain) involving real government employees and deaf people. This evaluation includes objective measurements from the system and subjective information from questionnaire

A survey on mouth modeling and analysis for Sign Language recognition

© 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR

A baseline system for the transcription of catalan broadcast conversation

The paper describes aspects, methods and results of the development of an automatic transcription system for Catalan broadcast conversation by means of speech recognition. Emphasis is given to Catalan language, acoustic and language modellingmethods and recognition. Results are discussed in context of phenomena and challenges in spontaneous speech, in particular regarding phoneme duration and feature space reduction.Postprint (published version

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

Tight Integrated End-to-End Training for Cascaded Speech Translation

A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation between source speech and target text. Such modeling suffers from error propagation between ASR and MT models. Direct speech translation is an alternative method to avoid error propagation; however, its performance is often behind the cascade system. To use an intermediate representation and preserve the end-to-end trainability, previous studies have proposed using two-stage models by passing the hidden vectors of the recognizer into the decoder of the MT model and ignoring the MT encoder. This work explores the feasibility of collapsing the entire cascade components into a single end-to-end trainable model by optimizing all parameters of ASR and MT models jointly without ignoring any learned parameters. It is a tightly integrated method that passes renormalized source word posterior distributions as a soft decision instead of one-hot vectors and enables backpropagation. Therefore, it provides both transcriptions and translations and achieves strong consistency between them. Our experiments on four tasks with different data scenarios show that the model outperforms cascade models up to 1.8% in BLEU and 2.0% in TER and is superior compared to direct models.Comment: 8 pages, accepted at SLT202