SpeechSense: An Interactive Sensor Platform for Speech Therapy

SpeechSense：用于言语治疗的交互式传感器平台

• 批准号: 10256832
• 负责人: Gianluca De Luca
• 金额: $ 25.46万
• 依托单位: ALTEC, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10256832
• 关键词: Accelerometer; Acoustics; Address; Adult; Affect; Algorithm Design; Algorithmic Software; Algorithms; American; Benign; Boston; Caring; Child; Clinic; Clinical; Clinical assessments; Communication; Computer software; Custom; Data; Derivation procedure; Development; Devices; Disease; Dysarthria; Dysphonia; Environment; Equipment; Evaluation; Foundations; Frequencies; Goals; Gold; Human; Hybrids; Impairment; Individual; Intervention; Language; Larynx; Lesion; Life; Measurement; Measures; Mental Health; Modality; Monitor; Motor; Movement; Muscle Tension; National Institute on Deafness and Other Communication Disorders; Neck; Noise; Outcome Measure; Parkinson Disease; Pathologist; Patient Self-Report; Patients; Pattern Recognition; Perception; Phase; Procedures; Quality of life; Research Personnel; Series; Severities; Signal Transduction; Small Business Innovation Research Grant; Software Design; Source; Speech; Speech Disorders; Speech Therapy; Surface; System; Technology; Testing; Therapeutic; Therapeutic Intervention; Time; Universities; Voice; Work; automated algorithm; base; coping; data acquisition; data fusion; design; evidence base; experience; feasibility testing; improved; indexing; innovation; insight; interest; microphone; new technology; novel; pressure; prevent; prospective; prototype; sensor; signal processing; social; sound; success; telehealth; tool; usability; vibration; vocal cord; voice therapy

This Phase I SBIR will develop SpeechSense™: An Interactive Sensor Platform for Speech Therapy of motor speech disorders impeding vocal communication for over 10M individuals in the US. Care for these individuals is primarily done in the clinic using either perceptual scales–which suffer from low inter-rater reliability–or sophisticated equipment for quantifying acoustic measures of speech—which is susceptible to conversational noise and therefore remains limited to controlled scripted recitations. As a result, quantitative measures for evaluating speech impairments during natural conversational interactions of daily life are unavailable to speech- language pathologists (SLPs), preventing them from obtaining a complete description of the presence, severity, and functional impact of a disorder, and limiting the carryover of therapeutic gains from the clinic into daily life. To meet this need, our team of experts in human measurement technology is partnering with leading motor speech researchers and SLPs at Boston University to develop a novel hybrid acoustic-accelerometer sensor paired to software for automated noise mitigation and derivation of vocal and articulatory measures for assessing natural conversational speech. Acoustic signals can provide robust articulatory measures of speech but struggle to isolate vocal measures amid ambient noise or the sound of other speakers; while accelerometer recordings are more robust to such noise when obtaining vocal measures of speech, but remain agnostic to the articulatory context of speech. Combining both sensor modalities therefore offers the unique opportunity to obtain vocal and articulatory measures during natural conversational interactions. Our Phase I plan will custom design a microcontroller, software, and firmware to integrate an acoustic microphone and accelerometer into a single, neck-worn sensor, which will be used to acquire a corpus of speech data from patients with hypokinetic dysarthria from Parkinson’s disease (PD) during conversational activities with and without various sources of noise. Using these data, we will develop a series of data fusion, pattern recognition, and signal processing algorithms to autonomously discriminate and mitigate noise sources of interest for deriving clinical measures of speech function (such as fundamental frequency, articulatory vowel space, speech rate, subglottal pressure, and others), validate them with respect to gold-standard clinical procedures, and test their reliability under different noise conditions. The sensor prototype and measurement software will be tested by our team of SLPs on PD patients with hypokinetic dysarthria to demonstrate that SpeechSense™ provides a feasible modality for both scripted and conversational assessment based on positive SLP and patient self-reports for usability, acceptability, and perceived value. This proof of concept will lay the foundation for developing a Phase II pre- commercial prototype with real-time algorithms and mobile software that will provide a new tool for SLPs to augment voice therapy, improve clinical assessment, and monitor treatment during natural conversational interactions where individuals experience the greatest need to improve quality of life.

第一阶段SBIR将开发SpeechSense™：一种用于运动言语言语治疗的交互式传感器平台 在美国，超过1000万人患有阻碍声音交流的疾病。照顾这些人是 主要是在临床上使用感知量表--评估者间可靠性低--或 用于量化语音声学测量的复杂设备， 噪音，因此仍然局限于控制脚本背诵。因此，量化措施， 在日常生活的自然对话互动中评估言语障碍是无法获得的， 语言病理学家（SLP），阻止他们获得一个完整的描述的存在，严重程度， 和功能的影响，并限制从临床到日常的治疗收益结转 生活为了满足这一需求，我们的人体测量技术专家团队与领先的电机制造商合作， 语音研究人员和SLP在波士顿大学开发一种新的混合声加速度传感器 与自动降噪软件配对，并导出声音和发音措施， 评估自然的会话语音。声学信号可以提供语音的鲁棒发音测量 但努力在环境噪声或其他扬声器的声音中隔离声音测量;而加速度计 当获得语音的声音测量时，录音对这种噪声更鲁棒，但对语音的声音测量保持不可知。 发音语境因此，将两种传感器模式结合起来， 在自然的对话互动中获得声音和发音的措施。我们的第一阶段计划 设计微控制器、软件和固件，将声学麦克风和加速度计集成到 单个颈戴式传感器，将用于从运动功能减退患者获取语音数据语料库 帕金森氏病（PD）的构音障碍，在有和没有各种来源的对话活动， 噪声利用这些数据，我们将开发一系列的数据融合，模式识别，和信号处理 用于自主辨别和减轻感兴趣的噪声源以导出临床测量的算法 语音功能（如基频，发音元音间距，语速，声门下压， 和其他），根据金标准临床程序对其进行验证，并在 不同的噪音条件。传感器原型和测量软件将由我们的SLP团队进行测试 对患有运动功能减退性构音障碍的PD患者进行研究，以证明SpeechSense™为 基于积极的SLP和患者自我报告的脚本和对话评估， 可接受性和感知价值。这一概念验证将为开发第二阶段预 商业原型与实时算法和移动的软件，将提供一个新的工具，为SLP， 增强语音治疗，改善临床评估，并在自然对话期间监测治疗 在这种互动中，个人最需要提高生活质量。