The neural mechanisms underlying the ability of human listeners to recognize speech in the presence of background noise are still imperfectly understood. However, there is mounting evidence that the medial olivocochlear system plays an important role, via efferents that exert a suppressive effect on the response of the basilar membrane. The current paper presents a computer modeling study that investigates the possible role of this activity on speech intelligibility in noise. A model of auditory efferent processing [Ferry, R. T., and Meddis, R. (2007). J. Acoust. Soc. Am. 122, 3519-3526] is used to provide acoustic features for a statistical automatic speech recognition system, thus allowing the effects of efferent activity on speech intelligibility to be quantified. Performance of the "basic" model (without efferent activity) on a connected digit recognition task is good when the speech is uncorrupted by noise but falls when noise is present. However, recognition performance is much improved when efferent activity is applied. Furthermore, optimal performance is obtained when the amount of efferent activity is proportional to the noise level. The results obtained are consistent with the suggestion that efferent suppression causes a "release from adaptation" in the auditory-nerve response to noisy speech, which enhances its intelligibility.

Stimulation of the olivocochlear bundle reduces basilar membrane displacement, driven auditory nerve activity, and compound action potential (CAP) response to acoustic stimulation. These effects were simulated using a computer model of the auditory periphery. The model simulates the medial efferent activity by attenuating the basilar membrane response. The model was evaluated against three animal studies reporting measurements at three levels of the auditory system; basilar membrane, single auditory nerve fibers and whole auditory nerve CAP. The CAP data included conditions where tones were masked by noise and "unmasked" by stimulation of the olivocochlear bundle. The model was able to simulate the data both qualitatively and quantitatively. As a consequence, it may be a suitable platform for studying the contribution of the efferent system to auditory processing of more complex auditory sounds in distracting backgrounds.

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A central question in auditory scene analysis is how we are able to follow speech against a background of interfering noise. The question is particularly acute for artificial speech recognition algorithms and the hearing impaired. The medial efferent system has been suggested as one contributor to our ability to hear speech in noise. We have recently added efferent suppression to our model of the auditory periphery and evaluated it against physiological observations at the level of the basilar membrane and auditory nerve. We have also replicated a study using compound action potentials where a tone in a noise background became more salient when the efferent system was artificially activated. Visual representations of the computed auditory nerve response to speech in noise show considerable improvement when the efferent system is activated. Tests using the auditory model as a front end to a connected-word recognition algorithm also showed improved performance in the presence of noise when efferent effects were included. The benefits of efferent suppression include reduced compression and extension of the dynamic range of individual auditory nerve fibers.