Perception of Paralinguistic Traits in Synthesized Voices

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  • Alice Emily Baird
  • Stina Hasse Jørgensen
  • Emilia Parada-Cabaleiro
  • Simone Hantke
  • Nicholas Cummins
  • Bjorn Schuller
Along with the rise of artificial intelligence and the internet-of-things, synthesized voices are now common in daily–life, providing us with guidance, assistance, and even companionship. From formant to concatenative synthesis, the synthesized voice continues to be defined by the same traits we prescribe to ourselves. When the recorded voice is synthesized, does our perception of its new machine embodiment change, and can we consider an alternative, more inclusive form? To begin evaluating the impact of aesthetic design, this study presents a first–step perception test to explore the paralinguistic traits of the synthesized voice. Using a corpus of 13 synthesized voices, constructed from acoustic concatenative speech synthesis, we assessed the response of 23 listeners from differing cultural backgrounds. Evaluating if the perception shifts from the known ground–truths, we asked listeners to assigned traits of age, gender, accent origin, and human–likeness. Results present a difference in perception for age and human–likeness across voices, and a general agreement across listeners for both gender and accent origin. Connections found between age, gender and human–likeness call for further exploration into a more participatory and inclusive synthesized vocal identity.
TitelProceedings of the 12th International Audio Mostly Conference : Augmented and Participatory Sound and Music Experiences : AM '17
Antal sider5
Udgivelses stedNew York
ForlagAssociation for Computing Machinery
ISBN (Elektronisk)9781450353731
StatusUdgivet - 2017
BegivenhedAudio Mostly: Augmented and Participatory Sound/Music Experiences - Queen Mary University of London , London, Storbritannien
Varighed: 23 aug. 201726 aug. 2017


KonferenceAudio Mostly
LokationQueen Mary University of London

ID: 195758688