Prosodyspeech: Towards Advanced Prosody Model for Neural Text-to-Speech

Yi, Yuan-Hao; Pan, Shifeng; Wang, Xi; Xiao, Yujia

doi:10.1109/icassp43922.2022.9746744

Cited by 4 publications

(1 citation statement)

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“…In [30], the acoustic prosody features were chosen in a Wizard of Oz setup, but several techniques for automatic prosody generation, at least for semantic speech, have been developed. Such techniques can be rule-based [11,34,35] or neural network-based [36][37][38][39][40].…”

Section: Literature Reviewmentioning

confidence: 99%

The Impression of Phones and Prosody Choice in the Gibberish Speech of the Virtual Embodied Conversational Agent Kotaro

Gonzalez,

Lo,

Mizuuchi

2023

Applied Sciences

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The number of smart devices is expected to exceed 100 billion by 2050, and many will feature conversational user interfaces. Thus, methods for generating appropriate prosody for the responses of embodied conversational agents will be very important. This paper presents the results of the “Talk to Kotaro” experiment, which was conducted to better understand how people from different cultural backgrounds react when listening to prosody and phone choices for the IPA symbol-based gibberish speech of the virtual embodied conversational agent Kotaro. It also presents an analysis of the responses to a post-experiment Likert scale questionnaire and the emotions estimated from the participants’ facial expressions, which allowed one to obtain a phone embedding matrix and to conclude that there is no common cross-cultural baseline impression regarding different prosody parameters and that similarly sounding phones are not close in the embedding space. Finally, it also provides the obtained data in a fully anonymous data set.

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