A computer model of auditory efferent suppression: Implications for the recognition of speech in noise

Abstract

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.

Modeling the Anti-masking Effects of the Olivocochlear Reflex in Auditory Nerve Responses to Tones in Sustained Noise

“…The relationship between estimated OHC gain reduction and elicitor level was nearly linear in the case of the Kawase et al data and for the present study, as shown in Figure 12A. Brown et al (2010) also predicted a linear relation between noise level and the efferent attenuation that maximized speech recognition. These results are consistent with OAE data suggesting a linear growth in MOCR strength (as estimated in units of percent of maximum OAE amplitude) with increased elicitor level (Backus and Guinan 2006).…”

“…For the purpose of this study, the gain was controlled independently in a single pathway, since the relationship between elicitor level and amount of gain reduction is not known. A similar approach has been taken in several other modeling studies (e.g., Ferry and Meddis 2007;Ghitza et al 2007;Messing et al 2009;Brown et al 2010). Conditions will be labeled by the amount of OHC gain reduction (ΔG in dB) associated with the C OHC value.…”

“…It is not known how shocking the bundle compares to eliciting the MOCR by sound; however, 40 dB was used as the upper limit of physiologically realistic gain change in the present study. A similar approach was taken by Brown et al (2010) to determine the amount of gain reduction needed to optimize identification of speech in noise, and by Ferry and Meddis (2007) to model physiological data at the level of the BM and for compound action potentials measured to tones in noise. However, this modeling approach has not been used to simulate detection and discrimination of tones in noise based on AN fibers with different SRs.…”

“…The filled triangles in Figure 12 show that decreasing OHC gain in an "optimal" way to maximize discriminability can dramatically increase the dynamic range relative to simulations with full OHC gain (ΔG=0 dB). For speech stimuli, this increase in dynamic range may lead to an improvement in signal-to-noise ratio and speech understanding in noise (Messing et al 2009;Brown et al 2010).…”

Evaluating Adaptation and Olivocochlear Efferent Feedback as Potential Explanations of Psychophysical Overshoot

“…A similar method is employed in these experiments; however, in addition to simulating hearing impairment, MOC feedback was simulated by adjusting C OHC . This approach is similar to other modeling studies (Ferry and Meddis 2007;Ghitza et al 2007;Messing et al 2009;Brown et al 2010) that model the basic effect of the MOCR by reducing the amount of cochlear nonlinearity.…”