Donghyeon Yun1 – firstname.lastname@example.org
Yi Shen2 – email@example.com
Jennifer J Lentz1 – firstname.lastname@example.org
1. Department of Speech, Language and Hearing Sciences, Indiana University Bloomington,
2631 East Discovery Parkway Bloomington, IN 47408
2. Department of Speech and Hearing Sciences, University of Washington,
1417 Northeast 42nd Street, Seattle, WA 98105-6246
Popular version of 2aCA11 – Measuring hearing aid compression algorithm preference with the Tympan
Presented at the 181st ASA Meeting
Click here to read the abstract
Speech understanding is challenging in background noise, especially for listeners with hearing loss. Although the use of hearing aids may be able to compensate for the loss of hearing sensitivity by amplifying incoming sounds, the target speech and background noise are often amplified together. In this way, hearing aids do not “boost” the signal with respect to the noise. Although hearing aids will make the sounds louder, common processing in these devices may even make the signal smaller relative to the noise. This is because the techniques used to boost soft sounds but not loud ones are nonlinear in nature. The amount of the signal relative to the noise is called the Signal to Noise Ratio, or the SNR. A lower SNR at the output of a hearing aid may make speech understanding more difficult. Thus, it is important to accurately assess the output SNR when prescribing hearing aids in an audiology clinic.
In this paper, we looked to see whether a specific technique used to determine the SNR at the output of a hearing aid gave accurate results. In this phase-inversion technique, the hearing aid’s response to a target speech sound (S) embedded in background noise (N) is recorded. We also collect responses with an “inverted” signal (S’) and an “inverted” noise (N’). By using these inverted signals, we can calculate the SNR at the output of the hearing aid.
It has been difficult to determine whether this technique gives an accurate estimate of SNR because there is no way to calculate the true SNR at the output of a hearing aid. However, we can do this with a simulated hearing aid. In the current study, we calculated true output SNR using the hearing aid simulation for a number of test conditions. We then compared these true values to values estimated using the phase-inversion technique under the same test conditions. The test conditions included: (1) various SNRs at the input of the simulated hearing aid, (2) hearing-aid configurations fitted to four typical profiles of hearing loss, (3) two types of background noise (two- and twenty-talker babble noises), and (4) various parameters of the nonlinear processing algorithm.
——————- The output SNRs estimated using the phase-inversion technique (symbols) agree well with the actual output SNRs (symbols) ——————-
In agreement with previous studies, the output SNR for the simulated hearing aid was different from the input SNR, and this mismatch between the output and input SNRs depended on the test condition. The differences between the actual and estimated output SNRs were very small, indicating satisfactory validity for the phase-inversion technique.