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) 设备而进行的。该项目遵循我们的初步工作，该工作令人信服地证明了从言语发音肌肉记录的表面肌电 (sEMG) 信号可以提供一种无需发声的新型有效交流形式。由于基于表面肌电图的语音识别不依赖于声道的声学激励，因此它很容易适用于识别含声（即用嘴）语音。因此，对于喉切除术患者来说，默声言语显然是一种替代的交流方式。该项目的目标是提供一个在 Android 移动设备（智能手机）上运行的预商用、可穿戴、默声语音识别 (SSR) 系统，该系统可以为非喉切除者提供在家庭、社区或通过电话进行免提、清晰通信的能力。该项目处于直接二期开发的有利位置。原理证明和风险降低已在两个方面实现：i）无线传感器设计已在基本原型中成功实现，该原型改进了从面部和颈部的 8 个关节肌记录 sEMG 信号的任务； ii) 迄今为止最先进的 SSR 引擎已被开发出来，可以准确识别无声连续语音，该语音来自对未受损的说话者以及 2 个喉切除患者进行测试的 2000 个单词词汇。第二阶段将通过减少仅在面部肌肉部位设置的必要传感器来推进这些技术，这些传感器将集成到预商用设备中，供非说话者进行喉切除术使用。 Aim 1 将把各个 sEMG 传感器整合到一个舒适的面部界面中，并将获取的信号合并到一个数据流中，以便通过蓝牙连接到运行 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，并在喉切除术受试者上进行评估。 Aim 2 将为喉切除术用户创建先进的 SSR 引擎，将面部所需的传感器数量从 8 个减少到 4 个，同时以低于 10% 的错误率实现 1000 个单词的识别性能。这项创新的影响在于，它为喉切除术用户提供了另一种语音形式，a) 克服了当前依赖麦克风的自动语音识别 (ASR) 系统的局限性，b) 与需要手持接触的电喉技术相比，无需手动操作，c) 不会像当前的发声假体那样存在清晰度差或需要手术干预和维护的问题，以及 d) 易于适应作为 AAC 设备的人机界面。