A computer model of auditory efferent suppression: implications for the recognition of speech in noise.
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.
Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom. g.brown@dcs.shef.ac.uk
Guy J
GJ
Brown
Robert T
RT
Ferry
Ray
R
Meddis
D000161:Acoustic Stimulation; D000818:Animals; D001307:Auditory Perception / Q000502:physiology*; D001489:Basilar Membrane / Q000502:physiology; D002415:Cats; D017626:Cochlear Nucleus / Q000502:physiology; D003198:Computer Simulation; D004525:Efferent Pathways / Q000502:physiology; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D009622:Noise; D009847:Olivary Nucleus / Q000502:physiology; D010363:Pattern Recognition, Automated; D046709:Pattern Recognition, Physiological / Q000502:physiology; D021641:Recognition, Psychology / Q000502:physiology; D013018:Sound Spectrography; D013060:Speech; D013067:Speech Perception / Q000502:physiology*; D049250:Speech Recognition Software