CHS: Small: Emotion-Aware Internet-of-Things Based on Analysis of Speech and Physiological Data
CHS:小型:基于语音和生理数据分析的情感感知物联网
基本信息
- 批准号:2147074
- 负责人:
- 金额:$ 49.78万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-08-15 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The Internet of Things describes a network of devices capable of connecting hundreds of billions of devices, and then sensing and communicating information required for a wide range of uses such as healthcare, vehicular systems, and industrial environments. As one of the most natural ways of communication, speech will increasingly be used as the primary form of interaction between humans and Internet of Things devices. In recent years, research has shown that there are clear links between the emotional and mental state of an individual and certain patterns in the individual's speech. If these patterns are detected in a timely fashion, it is possible to build emotion-aware Internet of Things solutions. This could be used to adapt a system to better meet the needs of the user, to prevent human error, to detect and prevent potentially malicious user activities, and to initiate medical interventions. Therefore, the overarching goal of this project is to advance speech-based emotion analysis to enable the design of such emotion-aware Internet of Things solutions. The project will also enrich the team's ongoing outreach and educational goals, including mentorship of minority and high-school students, revision of existing and development of new courses aligned with the research challenges in the project, and tight integration of research activities and undergraduate education.The technical challenges in the project are organized into three main thrusts. First, the project will develop and evaluate multi-modal emotion detection systems, where speech analysis is coupled with other physiological metrics such as heart rate, galvanic skin response, or skin temperature, to more accurately determine an individual's emotional state. Second, the work will apply the concept of topic modeling to perform context-aware analysis of speech data, which will also assist in differentiating short-term emotions (i.e., the current mood of an individual) from long-term emotions (e.g., depression). Topic modeling is an increasingly popular technique to learn, recognize, and extract the topics of spoken commands or conversations, providing additional context information for more accurate emotion analysis. The primary outcomes of the first two thrusts will be new insights into the design and development of emotion-aware systems. However, to achieve this goal, a comprehensive database containing speech and physiological data (annotated with the emotional states of the users) will be required, and therefore, the third thrust of the project will build such a database. When completed, this database will contain speech samples and other data from over 500 individuals and the database will be made available to the general scientific community to advance research beyond the team's institution.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.
物联网描述了一个能够连接数千亿设备的设备网络,然后感知和通信各种用途所需的信息,如医疗保健、车辆系统和工业环境。作为最自然的交流方式之一,语音将越来越多地被用作人类与物联网设备之间交互的主要形式。近年来,研究表明,个人的情绪和精神状态与个人语言的某些模式之间存在明显的联系。如果及时检测到这些模式,就有可能构建情感感知的物联网解决方案。这可以用来调整系统,以更好地满足用户的需求,防止人为错误,检测和防止潜在的恶意用户活动,并启动医疗干预。因此,本项目的首要目标是推进基于语音的情感分析,以实现这种情感感知的物联网解决方案的设计。该项目还将丰富团队正在进行的推广和教育目标,包括指导少数民族和高中生,修改现有课程和开发符合项目研究挑战的新课程,以及将研究活动与本科教育紧密结合起来。项目中的技术挑战分为三个主要方面。首先,该项目将开发和评估多模态情绪检测系统,其中语音分析与心率、皮肤电反应或皮肤温度等其他生理指标相结合,以更准确地确定个人的情绪状态。其次,这项工作将应用主题建模的概念来执行语音数据的上下文感知分析,这也将有助于区分短期情绪(即个人的当前情绪)和长期情绪(如抑郁)。主题建模是一种越来越流行的技术,用于学习、识别和提取口头命令或对话的主题,为更准确的情感分析提供额外的上下文信息。前两个重点的主要成果将是对情感感知系统的设计和开发的新见解。然而,要实现这一目标,将需要一个包含语音和生理数据(用用户的情绪状态注释)的综合数据库,因此,该项目的第三个重点将建立这样一个数据库。完成后,该数据库将包含来自500多人的语音样本和其他数据,该数据库将提供给一般科学界,以推进该团队所在机构以外的研究。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A Sequence-to-sequence Based Error Correction Model for Medical Automatic Speech Recognition
一种基于序列到序列的医疗自动语音识别纠错模型
- DOI:10.1109/bibm52615.2021.9669554
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Jiang, Yu;Poellabauer, Christian
- 通讯作者:Poellabauer, Christian
Medication Error Detection Using Contextual Language Models
- DOI:
- 发表时间:2022-01
- 期刊:
- 影响因子:0
- 作者:Yu Jiang;C. Poellabauer
- 通讯作者:Yu Jiang;C. Poellabauer
Automatic Speech Emotion Recognition Using Machine Learning: Digital Transformation of Mental Health
使用机器学习的自动语音情绪识别:心理健康的数字化转型
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Madanian, S.;Parry, D.;Adeleye, O.;Poellabauer, C.;Mirza, F.;Mathew, S.;Schneider, S.
- 通讯作者:Schneider, S.
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Christian Poellabauer其他文献
An Enhanced Energy Efficient Clustering Scheme for Prolonging the Lifetime of Heterogeneous Wireless Sensor Networks
用于延长异构无线传感器网络寿命的增强型节能集群方案
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
S. Dutt;O. S. Khanna;Kemal Akkaya;W. Dargie;Christian Poellabauer;K. Sohraby;D. Minoli;T. Znati;M. Yarvis;N. Kushalnagar;H. Singh;Li Qing;Qinqxin Zhu;Mingwen Wang;W. Heinzelman;A. Chandrakasan;B. Elbhiri;R. Saadane;D. Aboutajdine;Georgios Smaragdakis;Ibrahim Matta;Parul Saini;Ajay K. Sharma - 通讯作者:
Ajay K. Sharma
A multi-dilated convolution network for speech emotion recognition
用于语音情感识别的多扩张卷积网络
- DOI:
10.1038/s41598-025-92640-2 - 发表时间:
2025-03-10 - 期刊:
- 影响因子:3.900
- 作者:
Samaneh Madanian;Olayinka Adeleye;John Michael Templeton;Talen Chen;Christian Poellabauer;Enshi Zhang;Sandra L. Schneider - 通讯作者:
Sandra L. Schneider
Predicting long-term sleep deprivation using wearable sensors and health surveys.
使用可穿戴传感器和健康调查来预测长期睡眠不足。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:7.7
- 作者:
Rafael Trujillo;Enshi Zhang;J. Templeton;Christian Poellabauer - 通讯作者:
Christian Poellabauer
Reliable peak detection and feature extraction for wireless electrocardiograms
- DOI:
10.1016/j.compbiomed.2024.109478 - 发表时间:
2025-02-01 - 期刊:
- 影响因子:
- 作者:
Sajad Farrokhi;Waltenegus Dargie;Christian Poellabauer - 通讯作者:
Christian Poellabauer
Using Speech for the Diagnosis of Mild Traumatic Brain Injuries
- DOI:
10.1016/j.apmr.2016.09.087 - 发表时间:
2016-12-01 - 期刊:
- 影响因子:
- 作者:
Bryan (Ning) Xia;Bryan (Ning) Xia;Louis Daudet;Christian Poellabauer;Sandra Schneider - 通讯作者:
Sandra Schneider
Christian Poellabauer的其他文献
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{{ truncateString('Christian Poellabauer', 18)}}的其他基金
CHS: Small: Emotion-Aware Internet-of-Things Based on Analysis of Speech and Physiological Data
CHS:小型:基于语音和生理数据分析的情感感知物联网
- 批准号:
1908991 - 财政年份:2019
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
SCC-Planning: Coordinated Autonomous Operation of UAVs in Urban First Responder Scenarios
SCC-Planning:城市急救人员场景中无人机的协调自主操作
- 批准号:
1737496 - 财政年份:2017
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
EAGER: Feasibility of Using Speech as Biomarker for Concussions
EAGER:使用言语作为脑震荡生物标志物的可行性
- 批准号:
1450349 - 财政年份:2014
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
CI-New: An Open Speech Data Repository for Medical Prediction and Assessment of Neurological Disorders
CI-New:用于神经疾病医学预测和评估的开放语音数据存储库
- 批准号:
1405694 - 财政年份:2014
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
REU Site: Experimental Research on Wireless Networking
REU 站点:无线网络实验研究
- 批准号:
1062743 - 财政年份:2011
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
A Composable Hardware/Software Architecture for Instruction on Wireless Systems and Networks
用于无线系统和网络教学的可组合硬件/软件架构
- 批准号:
0942214 - 财政年份:2010
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
REU Site: Experimental Research on Wireless Networking
REU 站点:无线网络实验研究
- 批准号:
0754933 - 财政年份:2008
- 资助金额:
$ 49.78万 - 项目类别:
Continuing Grant
CSR-EHCS(EHS), SM: Collaborative Research: Integrated Energy-Aware Resource Scheduling for Wireless Real-Time Systems
CSR-EHCS(EHS)、SM:协作研究:无线实时系统的集成能源感知资源调度
- 批准号:
0834180 - 财政年份:2008
- 资助金额:
$ 49.78万 - 项目类别:
Standard Grant
CAREER: Judicious Resource Management in Wireless Systems
职业:无线系统中明智的资源管理
- 批准号:
0545899 - 财政年份:2006
- 资助金额:
$ 49.78万 - 项目类别:
Continuing Grant
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