We present first results of a study investigating the salience and typicality of prosodic markers in Swedish at turn ends for turn yielding and turn-keeping purposes. We performed an experiment where participants (N=32) were presented with conversational chunks and, after the audio ended, were asked to determine which of two speakers would speak next by clicking a picture on a screen. Audio stimuli were manipulated by (i) raising and (ii) lowering f 0 over the last 500 ms of a turn, (iii) speeding up or (iv) slowing down duration over the last 500 ms, and (v) raising and (vi) lowering the last pitch peak. In our data, out of all manipulations, increasing the speech rate was found to be the most disruptive (p<.005). Higher speech rate led to longer reaction times in turn-keeping, which were shorter in turn-yielding. Other manipulations did not significantly alter reaction times. The results presented here may be complemented with eye movement data, to further elucidate cognitive mechanisms underlying turn-taking behavior
Conversations play an important role in our daily life. During conversations, we usually talk from topic to topic automatically, smoothly and effortlessly. However, there are sometimes difficulties experienced in establishing and also acknowledging a new topic in an ongoing conversation. This fact shows that there are specific features and mechanisms to both producing and perceiving topic change, which play a prominent role in keeping continuous talk. The present study provides a multimodal analysis of the occurrence of topic shift in dyadic conversations which were recorded both for audio and video. The research was carried out on two stages, studying both visual and prosodic features of topic shift. On the first stage, the actual speakers gaze movements around topic shift were investigated while on the second stage, prosodic features such as pitch movement and intensity surrounding topic shift were measured. We wanted to know whether any of these features and their combination could be used as a cue to detect topic shift in a conversation. Focusing on detection and analysis of these features could be helpful for a better understanding of human-human as well as human-machine communication.
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