This study investigated the relationship between listener loudness tolerance and listener acceptable noise level (ANL). Twenty-five normal hearing adults completed loudness tolerance and acceptable noise level measures. Loudness tolerance was measured using a scaling technique. The acceptable noise levels were calculated from a procedure designed to quantify a listener's willingness to accept background noise while listening to speech. Pearson correlation confirmed that loudness tolerance and acceptable noise levels are not related.

Background The acceptable noise level (ANL) is a measurement used to quantify how much noise a person is willing to accept while listening to speech. ANL has been used to predict success with hearing aid use. However, physiological correlates of the ANL are poorly understood. One potential physiological correlate is the medial olivocochlear reflex (MOCR), which decreases the output of the cochlea and is thereby expected to increase noise tolerance. Purpose This study investigates the relationship between contralateral activation of the MOCR and tolerance of background noise. Research Design This study recruited 22 young adult participants with normal hearing. ANL was measured using the Arizona Travelogue recording under headphones presented at the most comfortable level (MCL) with and without multitalker babble noise. The MOCR strength was evaluated in all participants by measuring the cochlear microphonic (CM) with and without 40 dB sound pressure level contralateral broadband noise (CBBN). Data Analysis The CM observed in response to a 500-Hz tone was measured with and without CBBN, and changes in response to fast Fourier transform amplitude at 500 Hz were used as an indicator of the MOCR effect. The ANL was calculated by subtracting the maximum acceptable background noise level from the MCL. Participants were divided into two groups based on their ANL: low-ANL (ANL < 7 dB) and moderate-ANL (ANL ≥ 7 dB). An independent samples t-test was used to compare CM enhancement between low-ANL and moderate-ANL groups. Additionally, Pearson's correlation was used to investigate the relationship between the ANL and the MOCR effect on the CM. Results The results indicated that presentation of CBBN increased the CM amplitude, consistent with eliciting the MOCR. Participants in the low-ANL group had significantly larger CM enhancement than moderate-ANL participants. The results further revealed a significant correlation between the ANL and the MOCR effect on the CM. Conclusion This study suggests that a stronger MOCR, as assessed using CM enhancement, is associated with greater noise tolerance. This research provides a possible objective measure to predict background tolerance in patients and adds to our understanding about the MOCR function in humans.

The present study was a follow-up investigation to a previous study exploring the relationship between listeners' loudness tolerance and listeners' acceptable noise level among normally hearing adults. The present study compared the same two measures, but data were obtained from listeners with hearing loss; 12 adults with sensorineural hearing loss participated in a loudness tolerance measure using a scaling technique initially established for setting hearing aid output limits, in addition to an acceptable noise level measure. The acceptable noise level procedure used in this study quantified the listeners' acceptance of background noise while listening to speech. As with the research involving listeners with normal hearing, the Pearson correlation procedure indicated a lack of any statistically significant correlation between the two measures.

Although substitutions did not violate the 5-dB step size, the slope of a listener's hearing loss may be a factor in the inaccuracy of measurement during the substitution of warbled tones for pure tones.

The clinical analysis layer of transcription is part of a 'toolkit' approach for detailed analysis of communication samples. This article provides a rationale for using the clinical analysis strategy and is supported by several examples of how a clinical analysis layer can add to the understanding of communication errors or other clinical areas of interest. The examples used in this paper are drawn from the areas of stuttering and aural rehabilitation and demonstrate the utility of this clinical analysis layer of transcription.