Wearable Wisdom: An Intelligent Audio-Based System for Mediating Wisdom and Advice

Pataranutaporn, Pat; Gálvez, Tomás Vega; Yoo, Lisa Y.; Chhetri, Abishkar; Maes, Pattie

doi:10.1145/3334480.3383092

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…The latter can serve as motivators by depicting a personally meaningful mentor, adviser or supporter, exhibiting their facial features, voice and mannerisms. Easy availability of these characters has the potential to broaden benefits that may come from unrestricted access to mentors of an individual's choosing 20 . AI-generated characters can also provide access to educational content, regardless of location or resources.…”

Section: Ai-generated Charactersmentioning

confidence: 99%

“…Empirical evidence has shown the potential of conversational agents to induce positive behavioural and cognitive changes 48 . By combining conversational agents with AI-generated characters, personalized fitness apps could enable users to interact with and be guided by a personalized AI-generated coach or exercise buddy that resembles someone they find motivating or look up to as a role model 20 .…”

Section: Digital Health Counselling Only ~45% Of Individuals With Men...mentioning

confidence: 99%

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AI-generated characters for supporting personalized learning and well-being

et al. 2021

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he idea of computers generating content has been around since the 1950s. Some of the earliest attempts were focused on replicating human creativity by having computers generate visual art and music 1 . Unlike today's synthesized media, computer-generated content from the early era was far from realistic and easily distinguishable from that created by humans. It has taken decades and major leaps in artificial intelligence (AI) for generated content to reach a high level of realism.Generative and discriminative models are two different approaches to machines learning from data. Although discriminative models can identify a person in an image, generative models can produce a new image of a person that has never existed before. Recent leaps in generative models include generative adversarial networks (GANs) 2 . Since their introduction, models for AI-generated media, such as GANs, have enabled the hyper-realistic synthesis of digital content, including the generation of photorealistic images, cloning of voices, animation of faces and translation of images from one form to another 3-6 . The GAN architecture includes two neural networks, a generator and a discriminator. The generator is responsible for generating new content that resembles the input data, while the discriminator's job is to differentiate the generated or fake output from the real data. The two networks compete and try to outperform each other in a closed-feedback loop, resulting in a gradual increase of the realism of the generated output.GAN architectures can generate images of things that have never existed before, such as human faces 3,4 . However, StyleGAN is an example of a modifiable GAN that enables intuitive control of the facial details of generated images by separating high-level attributes like the identity of a person from low-level features such as hair or freckles, with few visible artefacts 4 . Researchers have also proposed an in-domain GAN inversion approach to enable the editing of GAN-generated images, allowing for de-aging or the addition of new facial expressions to existing photographs 7 . Meanwhile, transformers such as the ones used in the massive generative GPT-3 language model are already being shown to be successful for text-to-image generation 8 .

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Section: Ai-generated Charactersmentioning

confidence: 99%

Section: Digital Health Counselling Only ~45% Of Individuals With Men...mentioning

confidence: 99%

AI-generated characters for supporting personalized learning and well-being

et al. 2021

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AI-Generated Virtual Instructors Based on Liked or Admired People Can Improve Motivation and Foster Positive Emotions for Learning

Pataranutaporn

Leong

Danry

et al. 2022

2022 IEEE Frontiers in Education Conference (FIE)

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KinVoices: Using Voices of Friends and Family in Voice Interfaces

Chan

Gunasekaran

Pai

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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With voice user interfaces (VUIs) becoming ubiquitous and speech synthesis technology maturing, it is possible to synthesise voices to resemble our friends and relatives (which we will collectively call 'kin') and use them on VUIs. However, designing such interfaces and investigating how the familiarity of kin voices affect user perceptions remain under-explored. Our surveys and interviews with 25 users revealed that VUIs using kin voices were perceived as more engaging, persuasive and safer yet eerier than VUIs using common virtual assistant voices. We then developed a technology probe, KinVoice, an Alexa-based VUI that was deployed in three households over two weeks. Users set reminders using KinVoice, which in turn, gave the reminders in synthesised kin voices. This was to explore users' needs, uncover challenges involved and inspire new applications. We discuss design guidelines for integrating familiar kin voices into VUIs, applications that benefit from its usage, and implications for balancing voice realism and usability with security and diversification.

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Wearable Wisdom: An Intelligent Audio-Based System for Mediating Wisdom and Advice

Cited by 5 publications

References 21 publications

AI-generated characters for supporting personalized learning and well-being

AI-generated characters for supporting personalized learning and well-being

AI-Generated Virtual Instructors Based on Liked or Admired People Can Improve Motivation and Foster Positive Emotions for Learning

KinVoices: Using Voices of Friends and Family in Voice Interfaces

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