I-Corps: Real-time visual feedback for speech learning

I-Corps：语音学习的实时视觉反馈

• 批准号: 2408991
• 负责人: Tara McAllister
• 金额: $ 5万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2408991&HistoricalAwards=false
• 关键词: Corps; Real; time; visual; feedback

The broader impact of this I-Corps project is the development of a speech learning technology using visual-acoustic biofeedback for individuals interested in altering their speech patterns. When individuals are unable to pronounce certain sounds in a normal way or communicate successfully, there are negative impacts to participation in social and professional settings. Speech-language pathologists who treat speech disorders report that evidence-based treatment and resources for some sound pronunciations are scarce. Very few empirically-tested interventions are available that allow visualization of the acoustic speech signal, an evidence-based method to enhance learning. This software can also be implemented remotely via videoconferencing or telepractice, with the potential for significant impact on service providers, clinicians, and individuals. Ultimately, providing access to more efficient technology can improve communication and quality of life for many individuals. While some speech sounds have high salience in clinical and language learning contexts, there is a wide range of sounds that would broaden the impact of this technology in future stages of development. An additional application includes gender-affirming voice training.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a technology that provides visual-acoustic biofeedback to enhance learning and ultimately help individuals change their speech patterns. This technology is integrated in software that allows learners to view resonant frequencies of the vocal tract and compare them with a visual target representing the desired pronunciation. The technology uses the form of a real-time linear predictive coding spectrum of the speech signal. Manipulating certain characteristics of speech, such as resonant frequencies of the vocal tract, is a complex concept. This software can be used across a wide range of devices and via videoconferencing. The technology includes features to enhance an individual’s speech learning process, which includes adaptive difficulty in stimulus prompts, progress tracking, and gamification. Lastly, this speech visualization software will be integrated with a machine learning classifier developed to extend the utility of the technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目的更广泛的影响是使用视觉声学生物反馈对有兴趣改变其语音模式的个人的语音学习技术的发展。当个人无法以正常方式发音或成功交流时，参与社会和专业环境会产生负面影响。言语病理学家治疗语音疾病的病理学家报告说，循证待遇和一些声音发音的资源很少。很少有经验测试的干预措施可以可视化声音信号，这是一种基于证据的方法来增强学习。该软件也可以通过视频会议或远程处理来远程实施，并可能对服务提供者，临床医生和个人产生重大影响。最终，提供更高效的技术可以改善许多人的沟通和生活质量。尽管某些语音在临床和语言学习环境中具有很高的显着性，但有多种声音可以扩大该技术在未来发展阶段的影响。另一个应用程序包括对性别的语音培训。本I-Corps项目利用专家学习以及对行业生态系统的第一手投资来评估技术的翻译潜力。该解决方案是基于提供视觉声学生物反馈的技术的开发，以增强学习并最终帮助个人改变语音模式。该技术集成在软件中，该软件使学习者可以查看声带的谐音频率，并将其与代表所需发音的视觉目标进行比较。该技术使用语音信号的实时线性预测编码谱的形式。操纵语音的某些特征，例如声带的共鸣频率，是一个复杂的概念。该软件可以在各种设备上和视频会议上使用。该技术包括增强个人语音学习过程的功能，其中包括刺激提示，进度跟踪和游戏化的自适应难度。最后，该语音可视化软件将与开发的机器学习分类器集成在一起，以扩展技术的实用性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是宝贵的支持。