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™：用于运动语音言语治疗的交互式传感器平台 美国有超过 1000 万人患有阻碍声音交流的疾病，因此需要对这些人进行护理。 主要在诊所使用感知量表（其评估者间的可靠性较低）或 用于量化语音声学测量的复杂设备——易受会话影响 噪音，因此仍然仅限于受控的脚本背诵，因此，定量测量。 言语无法评估日常生活中自然对话互动中的言语障碍 语言病理学家（SLP），阻止他们获得对存在、严重程度、 以及疾病的功能影响，并限制将治疗成果从临床转移到日常中 为了满足这一需求，我们的人体测量技术专家团队正在与领先的电机合作。 波士顿大学的语音研究人员和 SLP 开发一种新型混合声学加速度传感器 与自动降噪软件以及导出声音和发音测量的软件配对 评估自然会话语音可以提供稳健的语音发音测量。 但在使用加速度计时很难在环境噪音或其他扬声器的声音中隔离声音测量； 当获得语音的声音测量时，录音对此类噪音更加稳健，但仍然不可知 因此，结合两种传感器模式提供了独特的机会。 在自然对话互动中获得声音和发音措施我们的第一阶段计划将定制。 设计一个微控制器、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，将用于获取运动机能减退患者的语音数据集 在有或没有各种来源的对话活动中，帕金森病 (PD) 引起的构音障碍 利用这些数据，我们将开发一系列数据融合、模式识别和信号处理。 自动区分和减轻感兴趣的噪声源以得出临床测量的算法 言语功能（例如基频、发音元音间距、语速、声门下压力、 等），根据黄金标准临床程序对其进行验证，并在以下条件下测试其可靠性 传感器原型和测量软件将由我们的 SLP 团队进行测试。 对患有低运动性构音障碍的 PD 患者进行研究，以证明 SpeechSense™ 提供了一种可行的治疗方式 基于积极的 SLP 和患者自我报告的可用性的脚本和对话评估， 该概念验证将为开发第二阶段预测试奠定基础。 具有实时算法和移动软件的商业原型将为 SLP 提供新工具 增强语音治疗、改善临床评估并在自然对话期间监测治疗 个人最需要提高生活质量的互动。