Yi Shen, Ph.D. - US grants

DESCRIPTION (provided by applicant): Collecting behavioral data efficiently is a significant challenge faced by many auditory scientists, especially those who conduct clinical or developmental research. The prolonged process of data collection is the bottleneck restricting how much information can be gained from a single test subject and how many participants can be included in a clinical study. The long-term goal of the proposed research is to increase the efficiency of behavioral data collection, making individualized estimation of auditory psychophysical models possible. As the first step toward this goal, the estimation of two important psychophysical models will be studied in detail. The two models are the auditory filter model, a model of spectral resolution, and the cochlear input-output function, a model of peripheral nonlinearity. The parameters of these models, such as the auditory-filter bandwidth and the compression ratio of the cochlear input-output function, have been shown to be reliable indicators of cochlear health and can predict supra-threshold listening deficits. Classical procedures to fit these models use threshold-based approaches: multiple thresholds are measured, and the psychophysical model of interest is fitted using those thresholds. For the proposed procedure, a Bayesian algorithm will used to ensure that the stimulus presented on each trial is the stimulus that maximally accelerates the rate of parameter convergence. This parameter-based approach allows the estimation of the auditory filter or the cochlear input-output function using a single experimental track and fewer than 200 trials. This is approximately ten times faster than procedures currently in use. In the proposed experiments, for both of these models, parameters estimated for normal hearing listeners using the proposed and threshold-based procedures will be compared to determine the relative reliability of the new procedure. The optimal configurations for the new procedure, e.g. how to initiate and terminate an experimental track, will be identified. Additionally, the procedure developed to estimate the auditory filter will be further developed to ensure its suitability for hearing-impaired listeners. Upon the completion of the proposed research program, user-friendly software packages will be made available to hearing research community for the estimation of the auditory filter and the cochlear input-output function. The outcome of this research is expected to have a strong and sustained impact on behavioral studies of hearing and hearing impairment. With the procedures to be developed, the fitting of fundamental auditory models for individual test subjects can become routine. This will open the door to a better understanding of the individual differences in hearing capability because scientists will be able to test more participants and/or make more measurements in their experiments. Moreover, given the efficiency of the procedures, it will be much easier for the future experimenters to track a listener's hearing characteristics longitudinally.

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.