Abstract not found

Abstract—This paper is concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative model for DA recognition in which segmentation and classification of DAs are carried out in parallel. Our approach to DA recognition is based on a switching dynamic Bayesian network (DBN) architecture. This generative approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. The switching DBN coordinates the recognition process by integrating the component models. The factored language model, which is estimated from multiple conversational data corpora, is used in conjunction with additional task specific language models. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. We have carried out experiments on the AMI corpus of multimodal meeting recordings, using both manually transcribed speech, and the output of an automatic speech recogniser, and using different configurations of the generative model. Our results indicate that the system performs well both on reference and fully automatic transcriptions. A further significant improvement in recognition accuracy is obtained by the application of the discriminative reranking approach based on conditional random fields.

Automatic segmentation is important for making multimedia archives comprehensible, and for developing downstream information retrieval and extraction modules. In this study, we explore approaches that can segment multiparty conversational speech by integrating various knowledge sources (e.g., words, audio and video recordings, speaker intention and context). In particular, we evaluate the performance of a Maximum Entropy approach, and examine the effectiveness of multimodal features on the task of dialogue segmentation. We also provide a quantitative account of the effect of using ASR transcription as opposed to human transcripts. 1

Multimedia meeting collections, composed of unedited audio and video streams, handwritten notes, slides, and electronic documents that jointly constitute a raw record of complex human interaction processes in the workplace, have attracted interest due to the increasing feasibility of recording them in large quantities, by the opportunities for information access and retrieval applications derived from the automatic extraction of relevant meeting information, and by the challenges that the extraction of semantic information from real human activities entails. In this paper, we present a succint overview of recent approaches in this field, largely influenced by our own experiences. We first review some of the existing and potential needs for users of multimedia meeting information systems. We then summarize recent work on various research areas addressing some of these requirements. In more detail, we describe our work on automatic analysis of human interaction patterns from audio-visual sensors, discussing open issues in this domain.

Abstract not found

Multimedia meeting collections, composed of unedited audio and video streams, handwritten notes, slides, and electronic documents that jointly constitute a raw record of complex human interaction processes in the workplace, have attracted interest due to the increasing feasibility of recording them in large quantities, by the opportunities for information access and retrieval applications derived from the automatic extraction of relevant meeting information, and by the challenges that the extraction of semantic information from real human activities entails. In this paper, we present a succint overview of recent approaches in this field, largely influenced by our own experiences. We first review some of the existing and potential needs for users of multimedia meeting information systems. We then summarize recent work on various research areas addressing some of these requirements. In more detail, we describe our work on automatic analysis of human interaction patterns from audio-visual sensors, discussing open issues in this domain.

Abstract not found

This paper is about the recognition and interpretation of multiparty meetings captured as audio, video and other signals. This is a challenging task since the meetings consist of spontaneous and conversational interactions between a number of participants: it is a multimodal, multiparty, multistream problem. We discuss the capture and annotation of the AMI meeting corpus, the development of a meeting speech recognition system, and systems for the automatic segmentation, summarisation and social processing of meetings, together with some example applications based on these systems.