{"title":"Speech production and perception data collection in R: A tutorial for web-based methods using speechcollectr.","authors":"Abbey L Thomas, Peter F Assmann","doi":"10.3758/s13428-024-02399-z","DOIUrl":null,"url":null,"abstract":"<p><p>This tutorial is designed for speech scientists familiar with the R programming language who wish to construct experiment interfaces in R. We begin by discussing some of the benefits of building experiment interfaces in R-including R's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that R is open source. We explain basic concepts of reactive programming in R, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new R package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.</p>","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-024-02399-z","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/3 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This tutorial is designed for speech scientists familiar with the R programming language who wish to construct experiment interfaces in R. We begin by discussing some of the benefits of building experiment interfaces in R-including R's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that R is open source. We explain basic concepts of reactive programming in R, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new R package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.
本教程专为熟悉 R 编程语言并希望用 R 构建实验界面的语音科学家设计。我们首先讨论了用 R 构建实验界面的一些好处,包括 R 现有的语音数据分析工具、平台独立性、适合基于网络的测试,以及 R 是开源的这一事实。我们解释了使用 R 进行反应式编程的基本概念,并通过详细介绍两个示例实验的开发过程来应用这些原则。第一个实验包括一个语音生成任务,要求参与者读出带有不同情绪的单词。第二个示例实验涉及语音感知任务,参与者在其中聆听录制的语音,并通过强迫选择题和置信度评级来识别说话者所表达的情绪。在本教程中,我们将介绍新的 R 软件包 speechcollectr,该软件包提供的功能非常适合基于网络的语音数据收集。该软件包简化了语音实验所需的代码,为记录参与者同意、收集参与者人口信息、录制音频、检查参与者的麦克风或耳机是否合适以及呈现音频刺激等常见任务提供了函数。最后,我们将介绍远程语音数据收集的一些困难,以及我们为应对这些挑战而纳入 speechcollectr 的解决方案。
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.