Auditory neural feedback as a basis for speech processing

The author describes the closed-loop ensemble-interval-histogram (EIH) model. It is constructed by adding a feedback system to the former, open-loop, EIH model (Ghitza, Computer, speech and Language, 1(2), pp.109-130, Dec. 1986). While the open-loop EIH is a computational model based upon the ascending path of the auditory periphery, the feedback system is motivated by the descending path and attempts to capture the functional contribution of the neural feedback mechanism in the auditory periphery. The capability of the resultant closed-loop EIH to preserve relevant phonetic information in quite and in noisy acoustic environments was measured quantitatively using the model as a front-end to a dynamic time warping (DTW), speaker-dependent, isolated-word recognizer. The database consisted of a 39 word alpha-digit vocabulary spoken by two male speakers, in different levels of additive white noise. In the absence of noise the recognition scores based on the close-loop EIH are comparable to those based on the open-loop EIH. However, recognition performance based on the closed-loop EIH does not decline as much as with the open-loop EIH at low signal-to-noise ratios. At SNR of 6 dB, the average correct-recognition score with the closed-loop EIH is 82%. This is equivalent to the recognition score obtained with the open-loop EIH at 10 dB SNR, a gain of 4 dB