Can a robot laugh with you?: Shared laughter generation for empathetic spoken dialogue

人と一緒に笑う会話ロボットを開発 --人に共感し、人と共生する会話AIの実現に向けて--. 京都大学プレスリリース. 2022-09-29.Spoken dialogue systems must be able to express empathy to achieve natural interaction with human users. However, laughter generation requires a high level of dialogue understanding. Thus, implementing laughter in existing systems, such as in conversational robots, has been challenging. As a first step toward solving this problem, rather than generating laughter from user dialogue, we focus on “shared laughter, ” where a user laughs using either solo or speech laughs (initial laugh), and the system laughs in turn (response laugh). The proposed system consists of three models: 1) initial laugh detection, 2) shared laughter prediction, and 3) laugh type selection. We trained each model using a human-robot speed dating dialogue corpus. For the first model, a recurrent neural network was applied, and the detection performance achieved an F1 score of 82.6%. The second model used the acoustic and prosodic features of the initial laugh and achieved a prediction accuracy above that of the random prediction. The third model selects the type of system’s response laugh as social or mirthful laugh based on the same features of the initial laugh. We then implemented the full shared laughter generation system in an attentive listening dialogue system and conducted a dialogue listening experiment. The proposed system improved the impression of the dialogue system such as empathy perception compared to a naive baseline without laughter and a reactive system that always responded with only social laughs. We propose that our system can be used for situated robot interaction and also emphasize the need for integrating proper empathetic laughs into conversational robots and agents

Speak like a wo(man) : a corpus linguistic and discourse analysis of gendered speech

Traditionally, studies in gender linguistics have been qualitative anecdotes which view gender on a dichotomous plane. Using normative research participants and small amounts of data, researchers in gender linguistics have made an array of assumptions about how men and women speak. Women are commonly thought of as being cooperative speakers while men are typically thought of as operating out of a power hierarchy. The study conducted in this thesis tests these assumptions by applying qualitative, corpus, and discourse analyses. A corpus of transcribed spoken conversational speech was compiled and measured for various linguistic and discourse elements which have historically been touted as paradigms of gendered speech. Using a demographically diverse sample of 185 participants, 50 hours of conversation were recorded and transcribed. From this corpus, various language elements such as theme, thematic conveyors, turn-taking, laughter, referencing, expletives, adjectives, hedges, `polite speech', and verbs were identified and measured for frequency of use by gender and by sexuality. The results from this study indicate that women and men do indeed use language with idiosyncratic linguistic and discourse features and at significantly different frequencies of use. When language use based upon sexuality was examined, the results indicate that queer men speak using a distinct language variety from women and heterosexual men. Thus, the planar dichotomy of gender and language does not appear to be a valid view due to the sharp divergence from the binary by a third group based upon sexuality. The results further demonstrate that using corpus analysis is an effective and optimal approach for analyzing some aspects of language use. The quantitative composition of corpus data has allowed research, to dispel and support many assumptions made by anecdotal observation. It is my hope that the results drawn from this study inspire other researchers to use corpus-based methodology in examining gender, sexuality, and language

A multi-dimensional analysis

This book provides an in-depth, multi-dimensional analysis of conversations between autistic adults. The investigation is focussed on intonation style, turn-taking and the use of backchannels, filled pauses and silent pauses. Previous findings on intonation style in the context of autism spectrum disorder (ASD) are contradictory, with claims ranging from characteristically monotonous to characteristically melodic intonation. A novel methodology for quantifying intonation style is used, and it is revealed that autistic speakers tended towards a more melodic intonation style compared to control speakers in the data set under investigation. Research on turn-taking (the organisation of who speaks when in conversation) in ASD is limited, with most studies claiming a tendency for longer silent gaps in ASD. No clear overall difference in turn-timing between the ASD and the control group was found in the data under study. There was, however, a clear difference between groups specifically in the earliest stages of dialogue, where ASD dyads produced considerably longer silent gaps than controls. Backchannels (listener signals such as mmhm or okay) have barely been investigated in ASD to date. The current analysis shows that autistic speakers produced fewer backchannels per minute (particularly in the early stages of dialogue), and that backchannels were less diverse prosodically and lexically. Filled pauses (hesitation signals such as uhm and uh) in ASD have been the subject of a handful of previous studies, most of which claim that autistic speakers produced fewer uhm tokens (specifically). It is shown that filled pauses were produced at an identical rate in both groups and that there was an equivalent preference of uhm over uh. ASD speakers differed only in the prosodic realisation of filled pauses. It is further shown that autistic speakers produced more long silent (within-speaker) pauses than controls. The analyses presented in this book provide new insights into conversation strategies and intonation styles in ASD, as reviewed in a summary analysis. The findings are discussed in the context of previous research, general characteristics of cognition in ASD, and the importance of studying communication in interaction and across neurotypes

A qualitative analysis of irony as humor in Japanese conversation

Tohoku University小野尚