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Related Experiment Video

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

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ChoiceKey: a real-time speech recognition program for psychology experiments with a small response set.

Christopher Donkin¹, Scott D Brown², Andrew Heathcote²

¹School of Psychology, University of Newcastle, 2308, Callaghan, NSW, Australia. chris.donkin@newcastle.edu.au.

Behavior Research Methods

|February 3, 2009

Summary

ChoiceKey, a new MATLAB package, automates spoken response collection in psychological experiments. This open-source tool enhances accuracy and efficiency, reducing manual coding for voice key data.

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Area of Science:

Cognitive Psychology
Human-Computer Interaction
Speech Technology

Background:

Psychological experiments traditionally use button presses for choice responses.
Spoken responses offer advantages for specific populations and experimental designs, but manual voice key coding is laborious and error-prone.

Purpose of the Study:

To introduce ChoiceKey, an open-source speech recognition package for MATLAB designed to automate the collection and analysis of spoken responses in psychological research.
To demonstrate the reliability and efficiency of ChoiceKey with minimal training for various speakers and small response sets.

Main Methods:

Development of ChoiceKey, a MATLAB-based speech recognition system.
Optimization of the system through training for specific response sets and individual speakers.
Evaluation of ChoiceKey's reliability and accuracy in collecting spoken choice responses.

Main Results:

ChoiceKey demonstrates reliability with minimal participant training for experiments involving two distinct spoken responses.
The package effectively reduces the need for manual coding, saving time and minimizing errors.
Analysis of individual differences and atypical responses provides insights for further development.

Conclusions:

ChoiceKey offers a reliable and efficient open-source solution for collecting spoken responses in psychological research.
The package has the potential to streamline experimental procedures and improve data quality, especially in studies involving vocalizations or specific participant groups.
Further extensions for larger response sets are being explored.