Speech-controlled automated matrix test (Knowledge Transfer Project) (T01#)
Speech-controlled automated matrix test (Knowledge Transfer Project) (T01#)
批准号:
396657276
负责人:
金额:
$0.0万
依托单位国家:
德国
项目类别:
CRC/Transregios (Transfer Project)
财政年份:
2018
资助国家:
德国
项目状态:
已结题
起止时间:
2017-12-31 至 2022-12-31
中文摘要
在一个超过17%的人口患有听力缺陷的社会中,可靠和易于获得的听力测试方法对于允许早期干预(例如使用听力设备)和评估此类干预的益处变得越来越重要。矩阵句子测试是一种精确的、国际上公认的方法,但到目前为止,需要有一个人类监督者在场,记录听者的反应。我们建议开发一个基于自动语音识别(ASR)的语音控制自动矩阵测试(SAMT)的原型,作为一种易于访问的听力测试。该系统将实现无监督的听力测试,这将大大提高其适用性,因为它需要的资源远远少于有监督的听力测试。SAMT将以Oldenburg句子测试(OLSA)为基础,该测试有16种语言版本。通过这个项目,我们将提供一个可以用两种语言进行测试的系统,以及一个快速适应新语言的开发工具包。在临床环境中,即使监事不会说听者的语言,该系统也能进行可靠的临床听力测试,从而消除了当今听力测试的另一个限制。该项目建立在一项成功的12年基础研究的基础上,该研究将鲁棒自动语音识别作为正常和听力受损听众听觉感知的模型。因此,该计划将把知识和技术应用于日常生活,以减轻听障人士的处境。
英文摘要
In a society in which more than 17% of the population suffer from a hearing deficit, reliable and easily accessible hearing test methods become more and more important to allow for an earlyintervention (e.g. with listening devices) and to assess the benefit of such interventions. Matrix sentence tests are an exact and internationally accepted method, but up to now require the presence of a human supervisor who logs the listener’s responses. We propose to develop aprototype for a Speech-controlled Automated Matrix Test (SAMT) as an easily accessible hearingtest, which is based on automatic speech recognition (ASR). The system will enable unsupervisedhearing testing which will significantly raise its applicability since it requires far less resources than its supervised counterpart. SAMT will be based on the Oldenburg Sentence test (OLSA), which is available in 16 languages. Through this project, we will provide a system that can perform the test in two languages, as well as a development kit for rapid adaptation to new languages. In a clinical setting, the system will enable a reliable clinical hearing test even when the supervisor does not speak the listener’s language, thereby lifting another restriction of today’s listening tests. The project builds on a successful 12-year funding of fundamental research on robust automatic speech recognition as a model for auditory perception of normal and hearing-impaired listeners. Hence, the project will transfer the knowledge and techniques into applications for daily life with the aim to alleviate the situation for persons with a hearing loss.
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