Automatic dialog acts recognition based on sentence structure

This paper deals with automatic dialog acts (DAs) recognition in Czech. Our work focuses on two applications: a multimodal reservation system and an animated talking head for hearing-impaired people. In that context, we consider the following DAs: statements, orders, investigation questions and other questions. The main goal of this paper is to propose, implement and evaluate new approaches to automatic DAs recognition based on sentence structure and prosody. Our system is tested on a Czech corpus that simulates a task of train tickets reservation. With lexical-only information, the classification accuracy is 91 %. We proposed two methods to include sentence structure information, which respectively give 94 % and 95 %. When prosodic information is further considered, the recognition accuracy reaches 96 %

Automatic Dialog Acts Recognition based on Words Clusters

This paper deals with automatic dialog acts (DAs) recognition in Czech. A Dialog act is defined by J. L. Austin [1] as a meaning of an utterance at the level of illocutionary force. The four following DAs are considered: statements, orders, yes/no questions and other questions. In our previous works, we proposed, implemented and evaluated two new approaches to automatic DAs recognition based on sentence structure. These methods have been validated on a Czech corpus that simulates a task of train tickets reservation. The main goal of this paper is to propose a new approach to solve the problem of lack of training data for automatic DA recognition. This approach clusters the words in the sentence into several groups using maximization of mutual information between two neighbor word classes. The classification accuracy of the unigram model (our baseline approach) is 91 %. The proposed method, a clustered unigram model, reduces the DA error rate by 12

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference