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在听力损失中理解语音的困难。 嘈杂的环境。言语理解困难的根源包括听觉和认知两方面的因素 不同的听众之间的差异。开发可以解释这些问题的语音清晰度模型 因素是必要的预测预期的语音识别性能与或不使用 助听器此外，如果这种模型可以有效地适合个人助听器用户，那么 助听器中的放大曲线可以根据用户的特定需求定制。然而，如此高效的 用于拟合语音可懂度模型的诊断过程还不可用。拟议研究 该计划将直接解决这个问题。该计划的长期目标是建立一个有效的诊断 助听器验配是一项非常重要的工作。作为实现这一目标的第一步， - 用于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）， 对个别听众将进行详细审查。贝叶斯自适应过程使用语音识别 任务，类似于临床言语测听，并且它允许估计言语的模型参数。 可懂度指数使用最少75个测试句子（大约12分钟的测试时间）。这些 估计的参数指示（1）在各种频带中有多少声学线索被用于 语音识别，（2）信噪比要求达到50%正确的性能水平 识别，以及（3）听者从语音中的上下文线索中受益。的相关性 模型参数听者的听觉和认知技能将系统地评估使用一组 不同年龄和听力状况的老年人。这些参数也将在两个常见的 听力条件：在时间波动背景下的语音识别，以及 视觉提示（即显示说话者的面部）。这些模型参数的依赖性 将调查常见的收听条件。此外，使用 贝叶斯自适应过程将被用来预测语音识别性能的辅助和无辅助 条件个性化的言语清晰度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。估计的模型也将用于优化 听力受损者的放大曲线，以及它与听力受损者偏好的 将检查扩增曲线。在拟议的研究计划完成后，将在 建立了一个全面的分析听众的语音识别性能。此外，一套 将提供一系列工具，以有效地将模型与个人听众相匹配，并优化放大效果 根据估计的模型参数绘制轮廓。

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.