This paper presents a semi-latent topic model for semantic domain detection in spoken language understanding systems. We use labeled utterance information to capture latent topics, which directly correspond to semantic domains. Additionally, we introduce an ’informative prior’ for Bayesian inference that can simultaneously segment utterances of known domains into classes and divide them from ou...

In recent years, the interest in research in speech understanding and spoken interaction has soared due to the emergence of virtual personal assistants. However, whilst the ability of these agents to recognise conversational speech is maturing rapidly, their ability to understand and interact is still limited. At the same time we have witnessed the development of the number of models based on m...

Natural language interaction has the potential to considerably enhance user experience, especially in mobile devices like smartphones and electronic tablets. Recent advances in software integration and efforts toward more personalization and context awareness have brought closer the long-standing vision of the ubiquitous intelligent personal assistant. Multiple voice-driven initiatives by a num...

Near-term spoken language systems will likely be limited in their interactive capabilities. To design them, we shall need to model how the presence or absence of speaker interaction influences spoken discourse patterns in different types of tasks. In this research, a comprehensive examination is provided of the discourse structure and performance efficiency of both interactive and noninteractiv...

We present a semi-supervised framework to construct spoken language understanding resources with very low cost. We generate context patterns with a few seed entities and a large amount of unlabeled utterances. Using these context patterns, we extract new entities from the unlabeled utterances. The extracted entities are appended to the seed entities, and we can obtain the extended entity list b...

This research characterizes the spontaneous spoken dis uencies typical of human-computer interaction, and presents a predictive model accounting for their occurrence. Data were collected during three empirical studies in which people spoke or wrote to a highly interactive simulated system as they completed service transactions. The studies involved within-subject factorial designs in which the ...

This paper describes our efforts towards utilizing multilingual spoken dialogue systems as an aid to second language acquisition. We argue that it is important for language students to have the opportunity to practice communication in a non-threatening environment, something that a computer can naturally provide. We envision a three-stage interaction focused around a specific topic of a lesson ...

This paper summarizes our previous works in modeling non-verbal behaviors for natural human-robot interaction (HRI) and discusses a path for integrating them into spoken dialogs. While some non-verbal behaviors can be considered “optional” elements to be added to a spoken dialog, some non-verbal behaviors substantially require a harmonized plan that simultaneously considers both spoken dialog a...

The recent explosive growth of voice over IP (VoIP) solutions calls for accurate modelling of VoIP traffic. This paper presents measurements of ON and OFF periods of VoIP activity from a significantly large database of VoIP call recordings consisting of native speakers speaking in some of the world’s most widely spoken languages. The impact of the languages and the varying dynamics of caller in...

In this paper, we investigate whether a dataset derived from a multi-purpose corpus such as the Spoken Dutch Corpus may be considered appropriate for developing a taxonomy of wh-questions, and a model of the way in which these questions are integrated in spoken discourse. We compare the results obtained from the Spoken Dutch Corpus with a similar analysis of a large random collection of FAQs fr...