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.

I阶段I SBIR将开发SpeechSense™：电动演讲语音治疗的交互式传感器平台 在美国，障碍阻碍了超过1000万个人的声音交流。照顾这些人是 首先在诊所中使用任何一种感知量表（患者间可靠性低）或 用于量化语音的声学测量的精致设备 - 容易对话 噪声，因此仍然仅限于受控的脚本朗诵。结果，定量措施 在日常生活的自然对话互动期间，评估语音障碍不可能进行语音 - 语言病理学家（SLP），阻止他们获得有关存在，严重性的完整描述 疾病的功能影响，并将诊所的治疗收益限制为每天 生活。为了满足这一需求，我们的人类测量技术专家团队正在与领先的电动机合作 波士顿大学的言语研究人员和SLP开发了一种新型混合原声加速器传感器 与软件配对，以自动降低噪音和声音和发音测量的推导 评估自然对话演讲。声学信号可以提供强大的语音旋转测量 但是，在环境噪音或其他扬声器的声音中，难以隔离声音措施；同时加速度计 在获得语音的声音措施时，记录对这种噪音更为强大，但仍然不可知 言语的发音环境。因此，将这两种传感器方式结合在一起，提供了独特的机会 在自然对话互动期间获得声音和发音措施。我们计划的阶段将定制 设计微控制器，软件和固件，以将声学麦克风和加速度计集成到一个 单，颈染的传感器，该传感器将用于从患有低迷患者的语音数据中获取语料库 帕金森氏病（PD）的构造障碍在有和没有各种来源的会话活动中 噪音。使用这些数据，我们将开发一系列数据融合，模式识别和信号处理 自主区分和减轻引导临床测量的噪声源的算法 语音功能（例如基本频率，关节元音空间，语音速率，宽大压力， 以及其他），验证它们有关金标准临床程序的验证，并在 不同的噪声条件。传感器原型和测量软件将由我们的SLP团队测试 关于PD患者的质心肌腱障碍患者，以证明SpecySense™为可行的方式提供了一种可行的方式 基于正面SLP和患者自我报告可用性的脚本和对话评估， 可接受性和感知价值。此概念证明将为开发II期预先建立基础 带有实时算法和移动软件的商业原型将为SLP提供新工具 增强语音疗法，改善临床评估并在自然对话期间监测治疗 个人经历了改善生活质量的最大需求的互动。