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

与年龄相关的听力损失的听众的主要抱怨是理解语言的困难。 嘈杂的环境。言语理解困难的根源包括听觉因素和认知因素 不同的听众会有所不同。开发可以解释这些问题的语音清晰度模型 对于预测预期的语音识别性能，无论是否使用 助听器。此外，如果这样的模型可以有效地适用于个人助听器用户，那么 助听器中的放大配置文件可以根据用户的特定需求进行定制。然而，如此高效 目前尚无适用于语音清晰度模型的诊断程序。拟议的研究 计划将直接解决这个问题。该计划的长期目标是建立一种有效的诊断 测试以实现个性化的助听器配件。作为实现这一目标的第一步，贝叶斯自适应程序 为了拟合广泛采用的语音清晰度模型，即语音清晰度指数(ANSI S3.5-1997)， 将对个别听众进行详细审查。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 可理解性指数只用了75个测试句子(大约12分钟的测试时间)。这些 估计的参数表明(1)不同频带中的声学提示被用于 语音识别，(2)达到50%正确性能水平所需的信噪比 以及(3)听者从言语中的语境线索中受益。这两者之间的关系 听者的听觉和认知技能的模型参数将使用一组 年龄和听力状况各不相同的老年人。参数也将在两个常见的 收听条件：时间波动背景下的语音识别，以及 视觉暗示(即说话者面部的显示)。这些组件的模型参数的依赖关系 将调查常见的收听情况。此外，使用 贝叶斯自适应过程将用于预测在辅助和非辅助情况下的语音识别性能 条件。个性化语音清晰度指数是否提供额外的预测能力 与标准模型相比，将对模型进行评估。估计的模型还将用于优化 个体听障患者的放大特性及其与听者偏好的关系 将检查放大配置文件。在拟议的研究方案完成后，将有一个模型 建立的目的是为听众的语音识别表现提供全面的分析。而且，一套 将提供一系列工具，以有效地使模型适合个别听众并优化放大 根据估计的模型参数进行配置。

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.