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Related Experiment Video

Updated: May 8, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Exploring expressivity and emotion with artificial voice and speech technologies.

Sandra Pauletto¹, Bruce Balentine, Chris Pidcock

¹Department of Theatre, Film and Television, University of York , UK.

Logopedics, Phoniatrics, Vocology

|September 13, 2013

Summary

This study explores artificial voice emotion in text-to-speech (TTS) for user interfaces. It developed a chatbot linking TTS and automatic speech recognition (ASR) to investigate speech emotion

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Area of Science:

Human-Computer Interaction (HCI)
Artificial Intelligence (AI)
Speech Synthesis and Recognition

Background:

Investigating emotion in synthesized audio-voice signals is crucial for advanced user interface design.
Current text-to-speech (TTS) technologies offer opportunities to imbue artificial voices with emotional expression.

Purpose of the Study:

To formulate a theory of expression for user interface design based on synthesized emotional audio.
To explore the artistic and psychological implications of artificial speech emotion.
To develop interdisciplinary tools for artificial voice research.

Main Methods:

Emotional parameters were defined using markup tags and refined with post-processing techniques.
Software was created to integrate a TTS synthesizer with an automatic speech recognition (ASR) engine, forming a conversational agent.
The capabilities of this artificial voice system were explored for various applications.

Main Results:

A chatbot capable of synthesized speech and auditory recognition was successfully developed.
The study explored both artistic and psychological dimensions of artificial voice emotion.
Initial findings suggest an emerging interdisciplinary ontology for artificial voices.

Conclusions:

The research provides a foundation for understanding and implementing emotional expression in synthesized speech.
Proposed HCI tools aim to facilitate future collaborative efforts in artificial voice development.
This work bridges disciplines including AI, HCI, and communication technologies.