Socializing with Olivia, the Youngest Robot Receptionist Outside the Lab

Niculescu, Andreea I.; Dijk, Betsy van; Nijholt, Anton; Limbu, Dilip Kumar; See, Swee Lan; Wong, Adison

doi:10.1007/978-3-642-17248-9_6

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…This subscale was used in one of our previous studies [58] and contains 4 items referring to the robot's capabilities to socialize with humans and to express personality traits, emotions and humor.…”

Section: Robot Social Skillsmentioning

confidence: 99%

Making Social Robots More Attractive: The Effects of Voice Pitch, Humor and Empathy

Niculescu

Dijk

Nijholt

et al. 2013

Int J of Soc Robotics

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243

115

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In this paper we explore how simple auditory/ verbal features of the spoken language, such as voice characteristics (pitch) and language cues (empathy/humor expression) influence the quality of interaction with a social robot receptionist. For our experiment two robot characters were created: Olivia, the more extrovert, exuberant, and humorous robot with a higher voice pitch and Cynthia, the more introvert, calmer and more serious robot with a lower voice pitch. Our results showed that the voice pitch seemed to have a strong influence on the way users rated the overall interaction quality, as well as the robot's appeal and overall enjoyment. Further, the humor appeared to improve the users' perception of task enjoyment, robot personality and speaking style while the empathy showed effects on the way users evaluated the robot's receptive behavior and the interaction ease. With our study, we would like to stress in particular the importance of voice pitch in human robot interaction and to encourage further research on this topic.

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Section: Robot Social Skillsmentioning

confidence: 99%

Making Social Robots More Attractive: The Effects of Voice Pitch, Humor and Empathy

Niculescu

Dijk

Nijholt

et al. 2013

Int J of Soc Robotics

Self Cite

243

115

View full text Add to dashboard Cite

show abstract

“…Since this category of features require a small amount of data to be extracted, they fit well with real-time applications as in the case of speaker identification in social settings [ 11 ]. In social human–robot interactions, fast response which is prone to some degree of error is considered better than accurate but delayed response (i.e., humans prefer fast response with some level of error better than accurate but delayed response) [ 12 ]. Additionally, short-term spectral features are easy to extract and are text and language independent.…”

Section: Related Workmentioning

confidence: 99%

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Al-Qaderi

Lahamer

Rad

2021

Sensors

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We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker’s gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker’s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

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“…As the capabilities of social robots have increased, so too has research on the application of social robots in receptionist roles [29,30]. Researchers have mainly explored interactions between humans and non-medical robot receptionists and investigated the robot's perceived sociability and functionality [29][30][31]. Variables examined have included perceived robot friendliness [29], speech recognition [31] and conversational dialogue [30].…”

Section: Personality Social Behaviours and The Medical Receptionistmentioning

confidence: 99%

“…Use of first name by the robot resulted in significantly more participants rating the robot as humane. In research by Hewitt et al [29] involving human medical receptionists, a personalised approach, such as using a patient's first name, resulted in increased rapport between the medical receptionist and patient. It might be that the robot's use of a participant's first name increased rapport, resulting in higher perceptions of humanity.…”

Section: Comparisons With Prior Workmentioning

confidence: 99%

Smiling and use of first-name by a healthcare receptionist robot: Effects on user perceptions, attitudes, and behaviours

Johanson

Ahn

Sutherland

et al. 2020

Paladyn, Journal of Behavioral Robotics

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AbstractRobots are now starting to be developed and used as receptionists in health applications. In this regard, it is important that robots’ behavioural skills are developed and researched so that people have appropriate and comfortable interactions. Smiling and use of first name are two more important social communication skills used during human interactions. While smiling and use of first name are often employed by robots in human interactions, the effect of these behaviours on perceptions of receptionist robots has not yet been experimentally investigated. This study explored the effects of robot smiling and robot use of the participant’s first name on perceptions of robot friendliness, mind, and personality, as well as attitudes and smiling behaviour. Forty participants interacted with a medical receptionist robot four times, in a two by two repeated measures design. Both smiling and use of first name had significant positive effects on participants’ perceptions of robot personality. Robot smiling also showed significant effects on participants’ overall attitudes towards robots, ratings of robot friendliness, and perceptions of the robot’s mind, and increased the frequency of participants’ own smiling. There were no significant interaction effects. Robot smiling in particular can enhance user perceptions of robots and increase reciprocal smiling.

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Socializing with Olivia, the Youngest Robot Receptionist Outside the Lab

Cited by 9 publications

References 20 publications

Making Social Robots More Attractive: The Effects of Voice Pitch, Humor and Empathy

Making Social Robots More Attractive: The Effects of Voice Pitch, Humor and Empathy

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Smiling and use of first-name by a healthcare receptionist robot: Effects on user perceptions, attitudes, and behaviours

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