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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr.

Abbey L Thomas¹, Peter F Assmann²

¹School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA. abbey.thomas@utdallas.edu.

Behavior Research Methods

|June 3, 2024

Summary

This summary is machine-generated.

This tutorial introduces speechcollectr, an R package for building speech experiment interfaces. It simplifies remote speech data collection for researchers familiar with R, enhancing accessibility and efficiency in speech science studies.

Keywords:

Audio recording Experimentation R programming Speech perception Speech production Web-based

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Area of Science:

Speech Science
Computational Linguistics
Human-Computer Interaction

Background:

Speech scientists often require custom interfaces for data collection.
Existing tools may lack flexibility or be platform-dependent.
R offers a powerful, open-source environment for data analysis and web applications.

Purpose of the Study:

To provide a tutorial for R users on constructing speech experiment interfaces.
To introduce the speechcollectr R package for streamlined web-based speech data collection.
To demonstrate the development of two sample speech experiments (production and perception).

Main Methods:

Explanation of reactive programming concepts in R.
Development of two sample experiments: emotional speech production and perception.
Introduction and application of the speechcollectr R package for data collection tasks.

Main Results:

The speechcollectr package simplifies common tasks in speech data collection.
Functions for participant consent, demographics, audio recording, and stimulus presentation are included.
The package addresses challenges associated with remote speech data collection.

Conclusions:

The speechcollectr R package facilitates the creation of web-based speech experiment interfaces in R.
It enhances the efficiency and accessibility of remote speech data collection for researchers.
This approach supports robust and reproducible speech science research.