Los modelos de diálogo y sus aplicaciones en sistemas de diálogo hombre-máquina: revisión de la literatura

Un proceso de diálogo entre humanos involucra una serie de actos del habla, cuya finalidad es transmitir los deseos, intenciones y creencias entre las partes involucradas en el mismo. El reconocimiento y clasificación de los actos del habla, la construcción de modelos basados en estos actos del habla y la evaluación de los modelos construidos, es el objetivo de los modelos de diálogo. Además, estos modelos, incorporados en un sistema informático, permiten la interacción hombre-máquina usando el habla para la solución de diversos problemas cotidianos como: comprar un tiquete de tren, reservar un vuelo, etc. En este artículo se recogen las diferentes técnicas para la construcción de modelos de diálogo y algunos de los diversos sistemas informáticos que surgieron a partir de ellos, con el fin de determinar la aplicabilidad de los modelos de diálogo en el proceso de captura de requisitos durante la fase de definición del ciclo de vida de una aplicación de software

On what happens in gesture when communication is unsuccessful

Previous studies found that repeated references in successful communication are often reduced, not only at the acoustic level, but also in terms of words and manual co-speech gestures. In the present study, we investigated whether repeated references are still reduced in a situation when reduction would not be beneficial for the communicative situation, namely after the speaker receives negative feedback from the addressee. In a director–matcher task (experiment I), we studied gesture rate, as well as the general form of the gestures produced in initial and repeated references. In a separate experiment (experiment II) we studied whether there might (also) be more gradual differences in gesture form between gestures in initial and repeated references, by asking human judges which of two gestures (one from an initial and one from a repeated reference following negative feedback) they considered more precise. In both experiments, mutual visibility was added as a between subjects factor. Results showed that after negative feedback, gesture rate increased in a marginally significant way. With regard to gesture form, we found little evidence for changes in gesture form after negative feedback, except for a marginally significant increase of the number of repeated strokes within a gesture. Lack of mutual visibility only had a significant reducing effect on gesture size, and did not interact with repetition in any way. However, we did find gradual differences in gesture form, with gestures produced after negative feedback being judged as marginally more precise than initial gestures. The results from the present study suggest that in the production of unsuccessful repeated references, a process different from the reduction process as found in previous studies in repeated references takes place, with speakers appearing to put more effort into their gestures after negative feedback, as suggested by the data trending towards an increased gesture rate and towards gestures being judged as more precise after feedback

Visualización del lenguaje a través de corpus

Digital version of the print publication, published in A Coruña: Universidade da Coruña, Servizo de Publicacións, 2010 (ISBN 978-84-9749-401-4)This book contains the papers presented at the Second International Conference on Corpus Linguistics held at the University of A Coruña in 2010 and organised by the MuStE group. The essays deal with different aspects of corpus linguistics both as a methodology and as a branch of Linguistics.[Abstract] The collection of essays we are presenting here are just a mere sample of the interest the topics relating to Corpus Linguistics have arisen everywhere. Such different topics as those related to Computational Linguistics found in “Obtaining computational resources for languages with scarce resources from closely related computationally-developed languages. The Galician and Portuguese case“ or “Corpus-Based Modelling of Lexical Changes in Manic Depression Disorders: The Case of Edgar Allan Poe” belonging to the field of Corpus and Literary Studies can be found in the ensuing pages. Almost all research areas can nowadays be investigated using Corpus Linguistics as a valid methodology. This is reason why Language Windowing through Corpora gathers papers dealing with discourse, variation and change, grammatical studies, lexicology and lexicography, corpus design, contrastive analyses, language acquisition and learning or translation. This work’s title aims at reflecting not only the great variety of topics gathered in it but also the worldwide interest awaken by the computer processing of language. In fact, researchers from many different institutions all over the world have contributed to this book. Apart from the twenty-two Spanish Universities, people from other Higher Education Institutions have authored and co-authored the essays contained here, namely, Russia, Venezuela, Brazil, UK, Finland, Portugal, Poland, Austria, Mexico, Thailand, Iran, the Netherlands, Belgium, Japan, Turkey, China, Italy, Malaysia, Romania and Sweden. All these essays have been alphabetically arranged, by the names of their authors, in two parts. Part 1 contains the papers by authors from A to K and Part 2, those of authors from L to Z

A Model to Predict Driver Task Performance When Interacting with In-Vehicle Speech Interfaces for Destination Entry and Music Selection.

Motor vehicle crashes were estimated to be the eleventh leading cause of death in United States in 2009. Using a speech interface to operate infotainment systems while driving can potentially reduce driver distraction. Unfortunately, evaluations of driver interfaces are often too late to make changes. An alternative approach is to model driver task performance when using speech interfaces and to use the model to predict system performance early in design when changes are easier to make. The purposes of this research are to understand how drivers interact with speech interfaces and based on that knowledge, develop and validate a simulation model of how drivers interact with speech interfaces to aid speech-interface development. To develop the simulation model, a survey and a driving simulator experiment were conducted to identify how these tasks are carried out and the values for the process parameters. First, using a survey, frequency data for tasks and methods, and the content in user-generated databases were collected to assure that real tasks and constraints are considered in the simulation model. Next, a driving simulator experiment was conducted to understand how drivers perform destination entry and music selection and to determine the time drivers need to construct utterances, the errors drivers make, and the probability of correction strategies are used for each type of error. Half of these data were used to create the simulation model structure and provide the model parameters for entering destinations and selecting music using speech. Finally, the simulation model was validated for these two tasks using the second half of the data from the previous experiment. This research provides a model to predict user task performance with speech interfaces in motor vehicles. Use of this model supports the design of safer and easier to use speech interfaces in vehicles that can minimize eyes-off-road time and should reduce crash risk, and thereby protect public health. This model can be exercised to examine alternative speech interface configurations months before a physical interfaces is available for user testing when changes are easier to make, which saves time, reduces cost, and improves the quality of the interface produced.PHDIndustrial HealthUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99777/1/loe_1.pd

Characterizing and predicting corrections in spoken dialogue systems

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machinelearning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99 % to 15.72%. 1