A number of cues, both linguistic and non-linguistic, have been found to mark discourse structure in conversation. This paper investigates the role of laughter, one of the most encountered non-verbal vocalizations in human communication, in the signalling of turn boundaries. We employ a corpus of informal dyadic conversations to determine the likelihood of laughter at the end of speaker turns and to establish the potential role of laughter in discourse organization. Our results show that, on average, about 10% of the turns are marked by laughter, but also that the marking is subject to individual variation, as well as effects of other factors, such as the type of relationship between speakers. More importantly, we find that turn ends are twice more likely than transition relevance places to be marked by laughter, suggesting that, indeed, laughter plays a role in marking discourse structure.

This study explores the role of speech register and prosody for the task of word segmentation. Since these two factors are thought to play an important role in early language acquisition, we aim to quantify their contribution for this task. We study a Japanese corpus containing both infant- and adult-directed speech and we apply four different word segmentation models, with and without knowledge of prosodic boundaries. The results showed that the difference between registers is smaller than previously reported and that prosodic boundary information helps more adult- than infant-directed speech.

The unsupervised discovery of linguistic terms from either continuous phoneme transcriptions or from raw speech has seen an increasing interest in the past years both from a theoretical and a practical standpoint. Yet, there exists no common accepted evaluation method for the systems performing term discovery. Here, we propose such an evaluation toolbox, drawing ideas from both speech technology and natural language processing. We first transform the speech-based output into a symbolic representation and compute five types of evaluation metrics on this representation: the quality of acoustic matching, the quality of the clusters found, and the quality of the alignment with real words (type, token, and boundary scores). We tested our approach on two term discovery systems taking speech as input, and one using symbolic input. The latter was run using both the gold transcription and a transcription obtained from an automatic speech recognizer, in order to simulate the case when only imperfect symbolic information is available. The results obtained are analysed through the use of the proposed evaluation metrics and the implications of these metrics are discussed.