Generative Modeling and Classification of Dialogs by a Low-level Turn-taking Feature
| Autor: | Vittorio Murino, Carlo Drioli, Anna Pesarin, Alessandro Perina, Alessandro Tavano, Marco Cristani |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Computer science
media_common.quotation_subject Feature extraction Markov process Dialog analysis generative modeling classification feature extraction computer.software_genre Machine learning symbols.namesake Artificial Intelligence Feature (machine learning) Conversation Dialog box Set (psychology) media_common business.industry Turn-taking Mixture model Generative model Signal Processing Human-human communication symbols Computer Vision and Pattern Recognition Artificial intelligence business computer Software Natural language processing Generative grammar |
| Popis: | In the last few years, a growing attention has been paid to the problem of human-human communication, trying to devise artificial systems able to mediate a conversational setting between two or more people. In this paper, we propose an automatic system based on a generative structure able to classify dialog scenarios. The generative model is composed by integrating a Gaussian mixture model and a (observed) Markovian influence model, and it is fed with a novel low-level acoustic feature termed steady conversational period (SCP). SCPs are built on duration of continuous slots of silence or speech, taking also into account conversational turn-taking. The interactional dynamics built upon the transitions among SCPs provides a behavioral blueprint of conversational settings without relying on segmental or continuous phonetic features, and may be important for predicting the evolution of typical conversational situations in different dialog scenarios. The model has been tested on an extensive set of real, dyadic and multi-person conversational settings, including a recent dyadic dataset and the AMI meeting corpus. Comparative tests are made using conventional acoustic features and classification methods, showing that the proposed scheme provides superior classification performances for all conversational settings in our datasets. Moreover, we prove that our approach is able to characterize the nature of multi-person conversation (namely, the role of the participants) in a very accurate way, thus demonstrating great versatility. |
| Databáze: | OpenAIRE |
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