Striving for Likability

Voice 2020, Nov 2020, Online Event

Over the next decade, the ambient computing era will eclipse the PC era. Information will be available everywhere, accessible frictionless and frustration-free through voice user interfaces. However, many of the old recipes for creating delight don’t apply to Voice-first or Voice-Only experiences.

Recent research shows that when communicating emotions, your voice matters more than your words. I.e., not WHAT you say, but HOW you say it, the linguistic and paralinguistic cues, most influences the emotion that is communicated when you talk. Interestingly, those emotions are more accurately perceived in a voice-only interaction, when compared to multi-modal.

This talk explores and demonstrates possibilities of a likable and unbiased engagement, by using affective computing technologies and emotions analytics (e.g., expressive speech synthesis, sentiment analysis, or readability statistics).

After briefly rehashing that a young, feminine, overly upbeat voice might not always be the most appropriate choice when synthesizing messages, we will turn to a more controversial, but interesting topic: genderless bots and an approach that is using machine learning models, including “Neural Word Embeddings”, to detect bias, before a bot relays it to its users.


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