Synthesising personality with neural speech synthesis

Shilin Gao*, Matthew Aylett, David Braude, Catherine Lai

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Matching the personality of conversational agent to the personality of the user can significantly improve the user experience, with many successful examples in text-based chatbots. It is also important for a voice-based system to be able to alter the personality of the speech as perceived by the users. In this pilot study, fifteen voices were rated using Big Five personality traits. Five content-neutral sentences were chosen for the listening tests. The audio data, together with two rated traits (Extroversion and Agreeableness), were used to train a neural speech synthesiser based on one male and one female voices. The effect of altering the personality trait features was evaluated by a second listening test. Both perceived extroversion and agreeableness in the synthetic voices were affected significantly. The controllable range was limited due to a lack of variance in the source audio data. The perceived personality traits correlated with each other and with the naturalness of the speech. Future work can be making a chatbot speak in a voice with a pre-defined or adaptive personality by using personality synthesis in speech together with text-based personality generation
Original languageEnglish
Publication statusE-pub ahead of print - 15 Sept 2023
Event24th Annual Meeting of the Special Interest Group on Discourse and Dialogue - Czech Republic, Prague
Duration: 11 Sept 202315 Sept 2023
https://sigdialinlg2023.github.io/index.html

Conference

Conference24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Abbreviated titleSIGDial 2023
CityPrague
Period11/09/2315/09/23
Internet address

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