权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Subvocal Speech for Augmentative and Alternative Communication

用于增强性和替代性交流的默声语音

基本信息

批准号：
9130174
负责人：
Gianluca De Luca
金额：
$ 71.4万
依托单位：
ALTEC, INC.
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2018-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9130174
关键词：
Acoustics Algorithms Android Articulators Augmentative and Alternative Communication Cellular Phone Communication Communication impairment Communications Media Communities Computer software Computers Custom Data Data Aggregation Development Devices Disease Electrolarynx Encapsulated Engineering Evaluation Face Facial Muscles Future Goals Hand Health Home environment Hydrogels Individual Joints Laryngectomy Lead Maintenance Methods Modeling Motor Muscle National Institute on Deafness and Other Communication Disorders Neck Operating System Operative Surgical Procedures Oral cavity Patients Performance Persons Phase Pilot Projects Positioning Attribute Quality of life Research Risk Running Sampling Signal Transduction Site Skin Small Business Innovation Research Grant Speech Speech Disorders Speech Recognition Software Stream Surface System Systems Development Technology Testing User-Computer Interface Vocabulary Vocabulary Test Voice Prostheses Wireless Technology Work alternative communication base communication device data acquisition design handheld mobile device human subject improved innovation mobile computing new technology novel programs prototype sensor signal processing speech recognition user-friendly vocalization

项目摘要

DESCRIPTION (provided by applicant): This Phase II SBIR is prompted by the need for more effective Augmentative and Alternative Communication (AAC) devices for persons unable to communicate through vocalization. The project follows our preliminary work, which convincingly demonstrated that surface electromyographic (sEMG) signals recorded from speech articulation muscles can provide a new and effective form of communication without vocalization. Because sEMG-based speech recognition does not rely on acoustic excitation of the vocal tract, it is readily applicable to recognizing subvocal (i.e. mouthed) speech. Subvocal speech is therefore an obvious alternative form of communication for patients with laryngectomy. The goal of this project is to deliver a pre-commercial, wearable, subvocal speech recognition (SSR) system operating on an Android mobile device (Smartphone) that can provide non-speakers with a laryngectomy the ability to produce hands-free, intelligible communication in the home, community, or over the phone. The project is well positioned for direct Phase II development. Proof-of- principal and reduced-risk have been achieved on two fronts: i) wireless sensor designs have been successfully implemented in a rudimentary prototype that improves the task of recording sEMG signals from 8 articulatory muscles of the face and neck; and ii) the most advanced SSR engine to date has been formulated to achieve accurate recognition of subvocal continuous speech from a 2000 word vocabulary tested on unimpaired speakers as well as from 2 people with laryngectomy. Phase II will advance these technologies by reducing the requisite sensor set to just facial muscle sites, which will be integrated into a pre-commercial device for use by non-speakers with a laryngectomy. Aim 1 will consolidate the individual sEMG sensors into a conformable facial interface and combine the acquired signals into a data stream for Bluetooth connectivity to the Android device running the SSR software. The resulting data acquisition system will be encapsulated, bench-tested, and evaluated on subjects with a laryngectomy. Aim 2 will create an advanced SSR engine for laryngectomy users that will reduce the requisite number of sensors from 8, to a sub-set of 4 on the face, while attaining a recognition performance for 1000 words at an error rate less than 10%. The impact of this innovation is that it provides laryngectomy users with an alternative form of speech that a) overcomes the limitations of current automated speech recognition (ASR) systems that are microphone dependent, b) is hands-free compared to electrolarynx technologies requiring handheld contact, c) does not suffer from poor intelligibility or the need for surgical interventio and maintenance as with current voice prostheses, and d) is readily adaptable as a man-machine interface for AAC devices.

描述(由申请人提供)：第二阶段SBIR是由于需要为无法通过发声进行交流的人提供更有效的增强和替代通信(AAC)设备而促成的。该项目遵循我们的初步工作，令人信服地证明，从语音发音肌肉记录的表面肌电信号可以提供一种新的、有效的交流形式，而不需要发声。由于基于表面肌电信号的语音识别不依赖于声道的声激励，因此它很容易适用于识别亚声(即嘴巴)语音。因此，对于喉切除术的患者来说，亚声言语是一种明显的替代沟通方式。该项目的目标是提供一种在Android移动设备(智能手机)上运行的预商用、可穿戴的亚声语音识别(SSR)系统，该系统可以为非说话者提供在家庭、社区或电话中进行免提、可理解的交流的能力。该项目处于良好的位置，可以直接进行二期开发。已经在两个方面实现了原则证明和降低的风险：i)无线传感器设计已经成功地在一个基本的原型中实施，该原型改进了记录面部和颈部8个关节肌肉的sEMG信号的任务；以及ii)迄今为止最先进的SSR引擎已经制定，以实现对2000个单词的连续语音的准确识别，这些词汇来自对未受损的说话者以及2名喉部切除术患者的测试。第二阶段将通过将必要的传感器设置减少到仅限于面部肌肉部位来推进这些技术，这些传感器将被集成到商业化前的设备中，供接受喉切除术的非扬声器使用。AIM 1将把单个表面肌电信号传感器整合到一个可协调的面部界面中，并将采集到的信号合并到数据流中，以便将蓝牙连接到运行SSR软件的Android设备。由此产生的数据采集系统将被封装、台架测试，并在喉部切除的受试者身上进行评估。AIM 2将为喉切除用户创建一个先进的SSR引擎，该引擎将把面部所需的传感器数量从8个减少到4个，同时实现对1000个单词的识别性能，错误率低于10%。这一创新的影响在于，它为喉切除用户提供了另一种语音形式，a)克服了当前依赖于麦克风的自动语音识别(ASR)系统的限制，b)与需要手持接触的电喉技术相比是免提的，c)不像目前的语音假体那样遭受较差的可理解性或手术干预和维护的需要，以及d)容易地被适应为AAC设备的人机界面。