Automated Pitch Convergence Improves Learning in a Social, Teachable Robot for Middle School Mathematics

Lubold, Nichola; Walker, Erin; Pon-Barry, Heather; Ogan, Amy

doi:10.1007/978-3-319-93843-1_21

Cited by 18 publications

(23 citation statements)

References 31 publications

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“…First, if a device is used by more than one person, how might it adaptively individualize its voice to match multiple people? Previous work in HRI has found that robots which engage in vocal entrainment-changing the pitch, speaking rate, intensity, and other features of speech to mirror the user-have positive social outcomes by improving perceptions of rapport, trustworthiness, and learning [42,43]. Such real-time voice adaptations between smart devices and the user or users might yield similar benefits.…”

Section: Open Questions About User-centric Voice Designmentioning

confidence: 99%

One Voice Fits All?

Cambre

Kulkarni

2019

Proc. ACM Hum.-Comput. Interact.

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When a smart device talks, what should its voice sound like? Voice-enabled devices are becoming a ubiquitous presence in our everyday lives. Simultaneously, speech synthesis technology is rapidly improving, making it possible to generate increasingly varied and realistic computerized voices. Despite the flexibility and richness of expression that technology now affords, today's most common voice assistants often have female-sounding, polite, and playful voices by default. In this paper, we examine the social consequences of voice design, and introduce a simple research framework for understanding how voice affects how we perceive and interact with smart devices. Based on the foundational paradigm of computers as social actors, and informed by research in human-robot interaction, this framework demonstrates how voice design depends on a complex interplay between characteristics of the user, device, and context. Through this framework, we propose a set of guiding questions to inform future research in the space of voice design for smart devices. CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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Section: Open Questions About User-centric Voice Designmentioning

confidence: 99%

One Voice Fits All?

Cambre

Kulkarni

2019

Proc. ACM Hum.-Comput. Interact.

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“…Thus, we suspect that the robot's social capabilities (such as nonverbal immediacy) can influence children's learning-as we have seen here and in multiple other studies discussed earlier-but that the influence of social behavior is moderated by other factors, such as the extent to which the robot's sociality is necessary for the learning activity to proceed smoothly (as in the case of conversation and storytelling-based activities), and the extent to which the robot's social behavior helps build rapport. This hypothesis is supported by Lubold and colleagues' recent work with middle school children and adults, in which a social robot with vocal entrainment contributed to increased learning on math tasks, though not increases in self-reported rapport (Lubold et al, 2016(Lubold et al, , 2018Lubold, 2017). Because the vocal entrainment served not only to match pitch and other vocal features, but also made the robot's text-to-speech voice much more expressive, these studies could not disentangle the effects of expressivity from entrainment-however, both expressivity and entrain increase the robot's social capabilities.…”

Section: Relation To Related Workmentioning

confidence: 69%

“…Some recent work has explored increasing prosodic synchrony in a speech-controlled child-robot game in order to promote cooperation and improve enjoyment (Chaspari and Lehman, 2016;Sadoughi et al, 2017). In addition, Lubold and colleagues developed several social voice-adaptive robots that adjust the pitch of the robot's text-to-speech voice to match that of its human interlocutor (Lubold et al, 2015(Lubold et al, , 2016(Lubold et al, , 2018Lubold, 2017). This vocal entrainment contributed to increased learning with undergraduate students as well as middle school students during math tasks, but did not increase self-reported rapport.…”

Section: Speech Entrainmentmentioning

confidence: 99%

“…Then, since an individual may vary their pitch in each utterance, the pitch of each utterance was also shifted up or down slightly based on whether the child's most recent utterance was higher or lower. Unlike Lubold and colleagues (Lubold et al, 2016(Lubold et al, , 2018, we did not adapt the pitch contour of the robot's speech. Because the base sounds for the robot's speech were recorded by a human (not flat text-tospeech as in Lubold et al's work), the sounds had their own pitch contours.…”

Section: Entrainmentmentioning

confidence: 99%

“…Some prior work with adults provides evidence in support of this hypothesis (Kidd and Breazeal, 2008;Lubold et al, 2016Lubold et al, , 2018Lubold, 2017); however, there is little work yet exploring a social robot's rapport and relationship with young children. Thus, in this paper, we explored whether enabling a social robot to perform rapport-building behaviors, including speech and behavior entrainment, and giving the robot an appropriate backstory regarding its abilities, could help establish rapport and generate positive interactions with children, which we hypothesized could improve children's learning and engagement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Effects of a Social Robot's Speech Entrainment and Backstory on Young Children's Emotion, Rapport, Relationship, and Learning

Kory-Westlund

Breazeal

2019

Front. Robot. AI

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In positive human-human relationships, people frequently mirror or mimic each other's behavior. This mimicry, also called entrainment, is associated with rapport and smoother social interaction. Because rapport in learning scenarios has been shown to lead to improved learning outcomes, we examined whether enabling a social robotic learning companion to perform rapport-building behaviors could improve children's learning and engagement during a storytelling activity. We enabled the social robot to perform two specific rapport and relationship-building behaviors: speech entrainment and self-disclosure (shared personal information in the form of a backstory about the robot's poor speech and hearing abilities). We recruited 86 children aged 3-8 years to interact with the robot in a 2 × 2 between-subjects experimental study testing the effects of robot entrainment Entrainment vs. No entrainment and backstory about abilities Backstory vs. No Backstory. The robot engaged the children one-on-one in conversation, told a story embedded with key vocabulary words, and asked children to retell the story. We measured children's recall of the key words and their emotions during the interaction, examined their story retellings, and asked children questions about their relationship with the robot. We found that the robot's entrainment led children to show more positive emotions and fewer negative emotions. Children who heard the robot's backstory were more likely to accept the robot's poor hearing abilities. Entrainment paired with backstory led children to use more of the key words and match more of the robot's phrases in their story retells. Furthermore, these children were more likely to consider the robot more human-like and were more likely to comply with one of the robot's requests. These results suggest that the robot's speech entrainment and backstory increased children's engagement and enjoyment in the interaction, improved their perception of the relationship, and contributed to children's success at retelling the story.

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