Detection of hot spots in poster conversations based on reactive tokens of audience

We present a novel scheme for indexing “hot spots” in conversations, such as poster sessions, based on the reaction of the audience. Specifically, we focus on laughters and non-lexical reactive tokens, which are presumably related with funny spots and interesting spots, respectively. A robust detection method of these acoustic events is realized by combining BIC-based segmentation and GMM-based classification, with additional verifiers for reactive tokens. Subjective evaluations suggest that hot spots associated with reactive tokens are consistently useful while those with laughters are not so reliable. Furthermore, we investigate prosodic patterns of those reactive tokens which are closely related with the interest level.