On the acoustics of overlapping laughter in conversational speech

The social nature of laughter invites people to laugh together. This joint vocal action often results in overlapping laughter. In this paper, we show that the acoustics of overlapping laughs are different from non-overlapping laughs. We found that overlapping laughs are stronger prosodically marked than non-overlapping ones, in terms of higher values for duration, mean F0, mean and maximum intensity, and the amount of voicing. This effect is intensified by the number of people joining in the laughter event, which suggests that entrainment is at work. We also found that group size affects the number of overlapping laughs which illustrates the contagious nature of laughter. Finally, people appear to join laughter simultaneously at a delay of approximately 500 ms; a delay that must be considered when developing spoken dialogue systems that are able to respond to users’ laughs

Convergence of laughter in conversational speech: effects of quantity, temporal alignment and imitation

A crucial feature of spoken interaction is joint activity at various linguistic and phonetic levels that requires fine-tuned coordination. This study gives a brief overview on how laughing in conversational speech can be phonetically analysed as partner-specific adaptation and joint vocal action. Laughter as a feature of social bonding leads to the assumption that when laughter appears in dialogues it is performed by both interlocutors. One possible type of convergence is when the conversational partners adapt their amount of laughter during their interaction. This partner-specific adaptation for laughter has been shown by Campbell (2007a). Persons, initially unknown to each other and without any negative attitude to the unknown partner, had to talk in ten consecutive 30-min conversations (interval of one week). With each conversation the level of familiarity increased which was also reflected by the increasing number of their laughs. Smoski & Bachorowski (2003) also showed that familiarity plays a big role for the number of laughs: friends laugh more often together than strangers do. But there is also evidence that the level of social distance plays a role for phonetic convergence/divergence in speech in terms of extended voice onset time in stop consonants (Abrego-Collier et al. 2011). Figure 1 illustrates the convergence effect in terms of the number of laughs for two speech corpora of task-based dyadic conversations (Anderson et al. 1991 for a map task; Baker & Hazan 2011 for a spot-the-difference game) with rather high correlation values. However, the familiarity effec

Exploring sequences of speech and laughter activity using visualisations of conversations

In this study, we analysed laughter in dyadic conversational interaction. We attempted to categorise patterns of speaking and laughing activity in conversation in order to gain more insight into how speaking and laughing are timed and related to each other. Special attention was paid to a particular sequencing of speech and laughter activity that is intended to invite an interlocutor to laugh (i.e. ‘invitation-acceptance’ scheme): the speaker invites the listener to laugh by producing a laugh after his/her own utterance, indicating that it is appropriate to laugh. We explored these kinds of sequences through visualisations of speech and laughter activity in conversations. Based on manual transcriptions of the HCRC Map Task corpus, we generated visualisations of speech and laughter activity. Using these visualisations, we found that people indeed show a tendency to adhere to the ‘invitation-acceptance’ scheme and that people tend to ‘wait’ to be invited to a shared laughter event rather than to ‘anticipate’ it. These speech-and-laugh-activity plots have shown to be helpful in analysing the interplay between laughing and speaking in conversation and can be used as a tool to enhance the researcher’s intuition on underresearched fields

Laughter in task-based settings:Whom we talk to affects how, when, and how often we laugh

Map task corpora are not typically used to study laughter, but they allow an interesting analysis of multiple factors such as familiarity between the participants, their gender, and eye contact. We conducted linear/generalized mixed-effects analysis to study if co-laughter, laughter rate, and the percentage of voiced frames in laughs are influenced by such factors. Our results show that, in conversations without eye contact, the gender of the participant was statistically relevant regarding laughter rate and the percentage of voiced frames, and the difference in gender was relevant regarding co-laughter. On the other hand, with eye contact, familiarity was statistically relevant with respect to co-laughter, laughter rate, and the percentage of voiced frames. Most of our results align and extend what has been previously found, except for voiced laughs between friends. This study emphasizes the highly variable character of laughter and its dependence on interlocutors' characteristics

Improving Meeting Inclusiveness using Speech Interruption Analysis

Meetings are a pervasive method of communication within all types of companies and organizations, and using remote collaboration systems to conduct meetings has increased dramatically since the COVID-19 pandemic. However, not all meetings are inclusive, especially in terms of the participation rates among attendees. In a recent large-scale survey conducted at Microsoft, the top suggestion given by meeting participants for improving inclusiveness is to improve the ability of remote participants to interrupt and acquire the floor during meetings. We show that the use of the virtual raise hand (VRH) feature can lead to an increase in predicted meeting inclusiveness at Microsoft. One challenge is that VRH is used in less than 1% of all meetings. In order to drive adoption of its usage to improve inclusiveness (and participation), we present a machine learning-based system that predicts when a meeting participant attempts to obtain the floor, but fails to interrupt (termed a `failed interruption'). This prediction can be used to nudge the user to raise their virtual hand within the meeting. We believe this is the first failed speech interruption detector, and the performance on a realistic test set has an area under curve (AUC) of 0.95 with a true positive rate (TPR) of 50% at a false positive rate (FPR) of <1%. To our knowledge, this is also the first dataset of interruption categories (including the failed interruption category) for remote meetings. Finally, we believe this is the first such system designed to improve meeting inclusiveness through speech interruption analysis and active intervention

Overlapped Speech Detection in Multi-Party Meetings

Detection of simultaneous speech in meeting recordings is a difficult problem due both to the complexity of the meeting itself and the environment surrounding it. The system proposes the use of gammatone-like spectrogram-based linear predictor coefficients on distant microphone channel data for overlap detection functions. The framework utilized the Augmented Multiparty Interaction (AMI) conference corpus to assess model performance. The proposed system offers enhancements over base line feature set models for classification