An active learning approach for statistical spoken language understanding

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-25085-9_67In general, large amount of segmented and labeled data is needed to estimate statistical language understanding systems. In recent years, different approaches have been proposed to reduce the segmentation and labeling effort by means of unsupervised o semi-supervised learning techniques. We propose an active learning approach to the estimation of statistical language understanding models that involves the transcription, labeling and segmentation of a small amount of data, along with the use of raw data. We use this approach to learn the understanding component of a Spoken Dialog System. Some experiments that show the appropriateness of our approach are also presented.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.García Granada, F.; Hurtado Oliver, LF.; Sanchís Arnal, E.; Segarra Soriano, E. (2011). An active learning approach for statistical spoken language understanding. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Verlag (Germany). 7042:565-572. https://doi.org/10.1007/978-3-642-25085-9_67S5655727042De Mori, R., Bechet, F., Hakkani-Tur, D., McTear, M., Riccardi, G., Tur, G.: Spoken language understanding: A survey. IEEE Signal Processing Magazine 25(3), 50–58 (2008)Fraser, M., Gilbert, G.: Simulating speech systems. Computer Speech and Language 5, 81–99 (1991)Gotab, P., Bechet, F., Damnati, G.: Active learning for rule-based and corpus-based spoken labguage understanding moldes. In: IEEE Workshop Automatic Speech Recognition and Understanding (ASRU 2009), pp. 444–449 (2009)Gotab, P., Damnati, G., Becher, F., Delphin-Poulat, L.: Online slu model adaptation with a partial oracle. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 2862–2865 (2010)He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 1836–1839 (2010)Riccardi, G., Hakkani-Tur, D.: Active learning: theory and applications to automatic speech recognition. IEEE Transactions on Speech and Audio Processing 13(4), 504–511 (2005)Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. International Journal of Pattern Recognition and Artificial Intelligence 16(3), 301–307 (2002)Tur, G., Hakkani-Tr, D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45, 171–186 (2005

Bringing together commercial and academic perspectives for the development of intelligent AmI interfaces

The users of Ambient Intelligence systems expect an intelligent behavior from their environment, receiving adapted and easily accessible services and functionality. This can only be possible if the communication between the user and the system is carried out through an interface that is simple (i.e. which does not have a steep learning curve), fluid (i.e. the communication takes place rapidly and effectively), and robust (i.e. the system understands the user correctly). Natural language interfaces such as dialog systems combine the previous three requisites, as they are based on a spoken conversation between the user and the system that resembles human communication. The current industrial development of commercial dialog systems deploys robust interfaces in strictly defined application domains. However, commercial systems have not yet adopted the new perspective proposed in the academic settings, which would allow straightforward adaptation of these interfaces to various application domains. This would be highly beneficial for their use in AmI settings as the same interface could be used in varying environments. In this paper, we propose a new approach to bridge the gap between the academic and industrial perspectives in order to develop dialog systems using an academic paradigm while employing the industrial standards, which makes it possible to obtain new generation interfaces without the need for changing the already existing commercial infrastructures. Our proposal has been evaluated with the successful development of a real dialog system that follows our proposed approach to manage dialog and generates code compliant with the industry-wide standard VoiceXML.Research funded by projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485), and DPS2008- 07029-C02-02.Publicad

Virtual Immortality: Reanimating Characters from TV Shows.

The objective of this work is to build virtual talking avatars of characters fully automatically from TV shows. From this unconstrained data, we show how to capture a character's style of speech, visual appearance and language in an e ort to construct an interactive avatar of the person and e ectively immortalize them in a computational model. We make three contributions (i) a complete framework for producing a generative model of the audiovisual and language of characters from TV shows; (ii) a novel method for aligning transcripts to video using the audio; and (iii) a fast audio segmentation system for silencing nonspoken audio from TV shows. Our framework is demonstrated using all 236 episodes from the TV series Friends [34] ( 97hrs of video) and shown to generate novel sentences as well as character specific speech and video

What’s the Matter? Knowledge Acquisition by Unsupervised Multi-Topic Labeling for Spoken Utterances

Systems such as Alexa, Cortana, and Siri app ear rather smart. However, they only react to predefined wordings and do not actually grasp the user\u27s intent. To overcome this limitation, a system must understand the topics the user is talking about. Therefore, we apply unsupervised multi-topic labeling to spoken utterances. Although topic labeling is a well-studied task on textual documents, its potential for spoken input is almost unexplored. Our approach for topic labeling is tailored to spoken utterances; it copes with short and ungrammatical input. The approach is two-tiered. First, we disambiguate word senses. We utilize Wikipedia as pre-labeled corpus to train a naïve-bayes classifier. Second, we build topic graphs based on DBpedia relations. We use two strategies to determine central terms in the graphs, i.e. the shared topics. One fo cuses on the dominant senses in the utterance and the other covers as many distinct senses as possible. Our approach creates multiple distinct topics per utterance and ranks results. The evaluation shows that the approach is feasible; the word sense disambiguation achieves a recall of 0.799. Concerning topic labeling, in a user study subjects assessed that in 90.9% of the cases at least one proposed topic label among the first four is a good fit. With regard to precision, the subjects judged that 77.2% of the top ranked labels are a good fit or good but somewhat too broad (Fleiss\u27 kappa κ = 0.27). We illustrate areas of application of topic labeling in the field of programming in spoken language. With topic labeling applied to the spoken input as well as ontologies that model the situational context we are able to select the most appropriate ontologies with an F1-score of 0.907

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

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Framework for Human Computer Interaction for Learning Dialogue Strategies using Controlled Natural Language in Information Systems

Spoken Language systems are going to have a tremendous impact in all the real world applications, be it healthcare enquiry, public transportation system or airline booking system maintaining the language ethnicity for interaction among users across the globe. These system have the capability of interacting with the user in di erent languages that the system supports. Normally when a person interacts with another person there are many non-verbal clues which guide the dialogue and all the utterances have a contextual relationship, which manage the dialogue as its mixed by the two speakers. Human Computer Interaction has a wide impact on the design of the applications and has become one of the emerging interest area of the researchers. All of us are witness to an explosive electronic revolution where lots of gadgets and gizmo's have surrounded us, advanced not only in power, design, applications but the ease of access or what we call user friendly interfaces are designed that we can easily use and control all the functionality of the devices. Since speech is one of the most intuitive form of interaction that humans use. It provides potential bene ts such as handfree access to machines, ergonomics and greater e ciency of interaction. Yet, speech-based interfaces design has been an expert job for a long time. Lot of research has been done in building real spoken Dialogue Systems which can interact with humans using voice interactions and help in performing various tasks as are done by humans. Last two decades have seen utmost advanced research in the automatic speech recognition, dialogue management, text to speech synthesis and Natural Language Processing for various applications which have shown positive results. This dissertation proposes to apply machine learning (ML) techniques to the problem of optimizing the dialogue management strategy selection in the Spoken Dialogue system prototype design. Although automatic speech recognition and system initiated dialogues where the system expects an answer in the form of `yes' or `no' have already been applied to Spoken Dialogue Systems( SDS), no real attempt to use those techniques in order to design a new system from scratch has been made. In this dissertation, we propose some novel ideas in order to achieve the goal of easing the design of Spoken Dialogue Systems and allow novices to have access to voice technologies. A framework for simulating and evaluating dialogues and learning optimal dialogue strategies in a controlled Natural Language is proposed. The simulation process is based on a probabilistic description of a dialogue and on the stochastic modelling of both arti cial NLP modules composing a SDS and the user. This probabilistic model is based on a set of parameters that can be tuned from the prior knowledge from the discourse or learned from data. The evaluation is part of the simulation process and is based on objective measures provided by each module. Finally, the simulation environment is connected to a learning agent using the supplied evaluation metrics as an objective function in order to generate an optimal behaviour for the SDS