Individualized Assessment and Prediction of Speech-Recognition Performance In Adults with Age-related Hearing Loss

年龄相关性听力损失成人的语音识别表现的个体化评估和预测

• 批准号: 10221416
• 负责人: Yi Shen
• 金额: $ 29.96万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10221416
• 关键词: Acoustics; Address; Adopted; Adult; Age; Algorithms; Americas; Assessment tool; Auditory; Clinical; Cognitive; Cognitive deficits; Communities; Complex; Cues; Custom; Dependence; Diagnostic; Diagnostic Procedure; Diagnostic tests; Elderly; Environment; Face; Frequencies; Future; Goals; Guidelines; Health Personnel; Healthcare; Hearing; Hearing Aids; Hour; Individual; Individual Differences; Laboratory Finding; Laboratory Research; Link; Lipreading; Measures; Methods; Modeling; Noise; Patients; Performance; Persons; Presbycusis; Procedures; Research; Role; Services; Short-Term Memory; Signal Transduction; Source; Speech; Speech Audiometry; Speech Intelligibility; Standard Model; Standardization; Testing; Time; Translating; Visual; Weight; base; clinical practice; cognitive skill; experience; hearing impairment; improved; indexing; individual patient; predictive modeling; programs; speech recognition; temporal measurement; tool

The main complaint from listeners with age-related hearing loss is the difficulty in understanding speech in noisy environments. The sources of the speech-understanding difficulty involve auditory and cognitive factors and vary from one listener to another. Developing models of speech intelligibility that can account for these factors is necessary for predicting expected speech-recognition performance with or without the use of a hearing aid. Moreover, if such models can be efficiently fitted to individual hearing-aid users, then the amplification profile in the hearing aid can be customized to the users' specific needs. However, such efficient diagnostic procedures for fitting models of speech-intelligibility are not yet available. The proposed research program will address this issue directly. The long-term goal of the program is to establish an efficient diagnostic test to enable individualized hearing-aid fitting. As a first step toward this goal, a Bayesian adaptive procedure for fitting a widely-adopted model of speech intelligibility, i.e. the Speech Intelligibility Index (ANSI S3.5-1997), to individual listeners will be examined in detail. The Bayesian adaptive procedure uses a speech recognition task, similar to clinical speech audiometry, and it allows the estimation of the model parameters for the Speech Intelligibility Index using as few as 75 test sentences (approximately 12 minutes of testing time). These estimated parameters indicate (1) how much acoustic cues in various frequency bands are being used for speech recognition, (2) the signal-to-noise ratio required to reach a performance level of 50% correct recognition, and (3) the listener's benefits from contextual cues in speech. The relationship between these model parameters to listener's auditory and cognitive skills will be systematically evaluated using a group of older adults with diverse age and hearing status. The parameters will also be studied under two common listening conditions: speech recognition in temporally fluctuating backgrounds, and speech recognition with visual cues (i.e. the display of the talker's face). The dependencies of the model parameters for these commonly occurring listening conditions will be investigated. Additionally, the estimated model using the Bayesian adaptive procedure will be used to predict speech-recognition performance under aided and unaided conditions. Whether the individualized Speech Intelligibility Index provides additional predictive power compared to the standard model will be evaluated. The estimated model will also be used to optimize the amplification profiles for individual hearing-impaired listeners, and its relationship to the listeners' preferred amplification profiles will be examined. Upon the completion of the proposed research program, a model will be established to provide comprehensive profiling of listeners' speech-recognition performance. Moreover, a set of tools will be made available to efficiently fit the model to individual listeners and to optimize the amplification profile according to the estimated model parameters.

与年龄相关的听力损失的听众的主要投诉是很难理解语音 嘈杂的环境。语音理解难度的来源涉及听觉和认知因素 从一个听众到另一个听众。开发可以说明这些的语音清晰度模型 在使用或不使用A的情况下预测预期语音识别表现的必要因素是必要的 助听器。此外，如果这些模型可以有效地适用于单个听力aid用户，那么 可以根据用户的特定需求定制助听器中的放大配置文件。但是，如此有效 尚无语音智能模型的诊断程序。拟议的研究 程序将直接解决此问题。该计划的长期目标是建立有效的诊断 测试以实现个性化的助听器配件。作为朝着这一目标的第一步，贝叶斯自适应程序 为了拟合广泛的语音清晰度模型，即语音清晰度指数（ANSI S3.5-1997）， 将对单个听众进行详细检查。贝叶斯自适应程序使用语音识别 任务，类似于临床语音测听，它允许估计语音的模型参数 可理解性索引使用少于75个测试句子（大约12分钟的测试时间）。这些 估计参数表明（1）在各种频段中使用多少声音提示用于 语音识别，（2）达到50％的性能水平所需的信噪比 认可，以及（3）听众从语音中的情境提示中受益。这些之间的关系 侦听器的听觉和认知技能的模型参数将使用一组 年龄多样化和听力状况的老年人。这些参数也将在两个共同的下进行研究 听力条件：在时间上波动的背景下的语音识别，并与语音识别 视觉提示（即说话者的脸显示）。这些模型参数的依赖关系 通常会研究听力条件。此外，使用 贝叶斯自适应程序将用于预测辅助和无助的言语识别表现 状况。个性化的语音可理解性指数是否提供额外的预测能力 与标准模型相比将进行评估。估计的模型还将用于优化 单个听力受损的听众的放大配置文件及其与听众的偏爱的关系 将检查放大概况。拟议的研究计划完成后，模型将是 建立旨在提供听众语音识别表现的全面分析。而且，一套 将提供工具，以有效地将模型适合单个听众并优化放大 根据估计的模型参数。