On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Voice-QA: evaluating the impact of misrecognized words on passage retrieval

Question Answering is an Information Retrieval task where the query is posed using natural language and the expected result is a concise answer. Voice-activated Question Answering systems represent an interesting application, where the question is formulated by speech. In these systems, an Automatic Speech Recognition module can be used to transcribe the question. Thus, recognition errors may be introduced, producing a significant effect on the answer retrieval process. In this work we study the relationship between some features of misrecognized words and the retrieval results. The features considered are the redundancy of a word in the result set and its inverse document frequency calculated over the collection. The results show that the redundancy of a word may be an important clue on whether an error on it would deteriorate the retrieval results, at least if a closed model is used for speech recognition.This work was carried out in the framework of TextEnterprise (TIN2009-13391-C04-03), Timpano (TIN2011-28169-C05-01), WIQEI IRSES (grant no. 269180) within the FP 7 Marie Curie People, FPU Grant AP2010-4193 from the Spanish Ministerio de Educaci´on (first author), and the Microcluster VLC/Campus on Multimodal Intelligent Systems (third author)Calvo Lance, M.; Buscaldi, D.; Rosso, P. (2012). Voice-QA: evaluating the impact of misrecognized words on passage retrieval. En Advances in Artificial Intelligence - IBERAMIA 2012. Springer Verlag (Germany). 462-471. https://doi.org/10.1007/978-3-642-34654-5_47S462471Buscaldi, D., Gómez, J.M., Rosso, P., Sanchis, E.: N-Gram vs. Keyword-Based Passage Retrieval for Question Answering. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 377–384. Springer, Heidelberg (2007)Harabagiu, S., Moldovan, D., Picone, J.: Open-Domain Voice-Activated Question Answering. In: 19th International Conference on Computational Linguistics (COLING 2002), pp. 1–7 (2002)Jones, K.: Index Term Weighting. Information Storage and Retrieval 9(11), 619–633 (1973)Moldovan, D., Paşca, M., Harabagiu, S., Surdeanu, M.: Performance Issues and Error Analysis in an Open-Domain Question Answering System. ACM Transactions on Information Systems (TOIS) 21(2), 133–154 (2003)Rosso, P., Hurtado, L.F., Segarra, E., Sanchis, E.: On the Voice-Activated Question Answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(1), 75–85 (2012)Sanderson, M., Paramita, M.L., Clough, P., Kanoulas, E.: Do User Preferences and Evaluation Measures Line Up? In: 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), pp. 555–562. ACM, New York (2010)Turmo, J., Comas, P., Rosset, S., Galibert, O., Moreau, N., Mostefa, D., Rosso, P., Buscaldi, D.: Overview of QAST 2009. In: Peters, C., Di Nunzio, G.M., Kurimo, M., Mandl, T., Mostefa, D., Peñas, A., Roda, G. (eds.) CLEF 2009. LNCS, vol. 6241, pp. 197–211. Springer, Heidelberg (2010

Language modelization and categorization for voice-activated QA

The interest of the incorporation of voice interfaces to the Question Answering systems has increased in recent years. In this work, we present an approach to the Automatic Speech Recognition component of a Voice-Activated Question Answering system, focusing our interest in building a language model able to include as many relevant words from the document repository as possible, but also representing the general syntactic structure of typical questions. We have applied these technique to the recognition of questions of the CLEF QA 2003-2006 contests.Work partially supported by the Spanish MICINN under contract TIN2008-06856-C05-02, and by the Vicerrectorat d’Investigació, Desenvolupament i Innovació of the Universitat Politècnica de València under contract 20100982.Pastor Pellicer, J.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2011). Language modelization and categorization for voice-activated QA. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042(7042):475-482. https://doi.org/10.1007/978-3-642-25085-9_56S47548270427042Akiba, T., Itou, K., Fujii, A.: Language model adaptation for fixed phrases by amplifying partial n-gram sequences. Systems and Computers in Japan 38(4), 63–73 (2007)Atserias, J., Casas, B., Comelles, E., Gónzalez, M., Padró, L., Padró, M.: Freeling 1.3: Five years of open-source language processing tools. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (2006)Carreras, X., Chao, I., Padró, L., Padró, M.: Freeling: An open-source suite of language analyzers. In: Proceedings of the 4th Language Resources and Evaluation Conference (2004)Castro-Bleda, M.J., España-Boquera, S., Marzal, A., Salvador, I.: Grapheme-to-phoneme conversion for the spanish language. In: Pattern Recognition and Image Analysis. Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, pp. 397–402. Asociación Española de Reconocimiento de Formas y Análisis de Imágenes, Benicàssim (2001)Chu-Carroll, J., Prager, J.: An experimental study of the impact of information extraction accuracy on semantic search performance. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 505–514. ACM (2007)Harabagiu, S., Moldovan, D., Picone, J.: Open-domain voice-activated question answering. In: Proceedings of the 19th International Conference on Computational Linguistics, COLING 2002, vol. 1, pp. 1–7. Association for Computational Linguistics (2002)Kim, D., Furui, S., Isozaki, H.: Language models and dialogue strategy for a voice QA system. In: 18th International Congress on Acoustics, Kyoto, Japan, pp. 3705–3708 (2004)Mishra, T., Bangalore, S.: Speech-driven query retrieval for question-answering. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 5318–5321. IEEE (2010)Padró, L., Collado, M., Reese, S., Lloberes, M., Castellón, I.: Freeling 2.1: Five years of open-source language processing tools. In: Proceedings of 7th Language Resources and Evaluation Conference (2010)Rosso, P., Hurtado, L.F., Segarra, E., Sanchis, E.: On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews PP(99), 1–11 (2010)Sanchis, E., Buscaldi, D., Grau, S., Hurtado, L., Griol, D.: Spoken QA based on a Passage Retrieval engine. In: IEEE-ACL Workshop on Spoken Language Technology, Aruba, pp. 62–65 (2006

Review of Research on Speech Technology: Main Contributions From Spanish Research Groups

In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en Tecnologías del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years

Using dependency parsing and machine learning for factoid question answering on spoken documents

This paper presents our experiments in question answering for speech corpora. These experiments focus on improving the answer extraction step of the QA process. We present two approaches to answer extraction in question answering for speech corpora that apply machine learning to improve the coverage and precision of the extraction. The first one is a reranker that uses only lexical information, the second one uses dependency parsing to score robust similarity between syntactic structures. Our experimental results show that the proposed learning models improve our previous results using only hand-made ranking rules with small syntactic information. Moreover, this results show also that a dependency parser can be useful for speech transcripts even if it was trained with written text data from a news collection. We evaluate the system on manual transcripts of speech from EPPS English corpus and a set of questions transcribed from spontaneous oral questions. This data belongs to the CLEF 2009 track on QA on speech transcripts (QAst).Peer ReviewedPostprint (author’s final draft

Modelado de lenguaje basado en categorías para Búsqueda de Respuesta dirigida por la Voz

En los últimos años ha aumentado el interés de realizar tareas de Búsqueda de Respuesta utilizando voz en vez de sentencias escritas. En este trabajo se presenta una aproximación para el sistema de Búsqueda de Respuesta dirigida por la Voz, focalizándose el trabajo realizado en construir modelos de lenguaje capaces de incluir el mayor numero de palabras relevantes para la tarea (extraídas del repositorio de documentos donde se realizará la búsqueda de la respuesta). Estos modelos de lenguaje, deberán representar también la estructura sintáctica general de cuestiones típicas que se pueden preguntar al sistema. Se ha aplicado las técnicas mostradas en preguntas pertenecientes a las ediciones 2003-2006 del CLEF QA Contest.Pastor Pellicer, J. (2011). Modelado de lenguaje basado en categorías para Búsqueda de Respuesta dirigida por la Voz. http://hdl.handle.net/10251/15872Archivo delegad

Multilingual Spoken Language Understanding using graphs and multiple translations

This is the author’s version of a work that was accepted for publication in Computer Speech and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Speech and Language, vol. 38 (2016). DOI 10.1016/j.csl.2016.01.002.In this paper, we present an approach to multilingual Spoken Language Understanding based on a process of generalization of multiple translations, followed by a specific methodology to perform a semantic parsing of these combined translations. A statistical semantic model, which is learned from a segmented and labeled corpus, is used to represent the semantics of the task in a language. Our goal is to allow the users to interact with the system using other languages different from the one used to train the semantic models, avoiding the cost of segmenting and labeling a training corpus for each language. In order to reduce the effect of translation errors and to increase the coverage, we propose an algorithm to generate graphs of words from different translations. We also propose an algorithm to parse graphs of words with the statistical semantic model. The experimental results confirm the good behavior of this approach using French and English as input languages in a spoken language understanding task that was developed for Spanish. (C) 2016 Elsevier Ltd. All rights reserved.This work is partially supported by the Spanish MEC under contract TIN2014-54288-C4-3-R and by the Spanish MICINN under FPU Grant AP2010-4193.Calvo Lance, M.; Hurtado Oliver, LF.; García-Granada, F.; Sanchís Arnal, E.; Segarra Soriano, E. (2016). Multilingual Spoken Language Understanding using graphs and multiple translations. Computer Speech and Language. 38:86-103. https://doi.org/10.1016/j.csl.2016.01.002S861033