Otoacoustic emission (OAE) data recorded for tone bursts presented separately and as a two-tone burst complex, that had been reported previously [Yoshikawa, H., Smurzynski, J., Probst R., 2000. Suppression of tone burst evoked otoacoustic emissions in relation to frequency separation. Hear. Res. 148, 95–106], were re-processed using the method of adaptive approximations by matching pursuit (MP). Two types of stimuli were applied to record tone burst OAEs (TBOAEs): (a) cosine-windowed tone bursts of 5-ms duration with center frequencies of 1, 1.5, 2 and 3kHz, (b) complex stimuli consisting of a digital addition of the 1-kHz tone burst together with either the 1.5-, 2- or 3-kHz tone burst. The MP method allowed decomposition of signals into waveforms of defined frequency, latency, time span, and amplitude. This approach provided a high time–frequency (t–f) resolution and identified patterns of resonance modes that were characteristic for TBOAEs recorded in each individual ear. Individual responses to single-tone bursts were processed off-line to form ‘sum of singles’ responses. The results confirmed linear superposition behavior for a frequency separation of two-tone bursts of 2kHz (the 1-kHz and 3-kHz condition). For the 1, 1.5-kHz condition, the MP results revealed the existence of closely positioned resonance modes associated with responses recorded individually with the stimuli differing in frequency by 500Hz. Then, the differences between t–f distributions calculated for dual (two-tone bursts) and sum-of-singles conditions exhibited mutual suppression of resonance modes common to both stimuli. The degree of attenuation depended on the individual pattern of characteristic resonance modes, i.e., suppression occurred when two resonant modes excited by both stimuli overlapped. It was postulated that the suppression observed in case of dual stimuli with closely-spaced components is due to mutual attenuation of the overlapping resonance modes.

Automatic speech recognition (ASR) is one of the most important research areas in the field of speech technology and research. It is also known as the recognition of speech by a machine or, by some artificial intelligence. However, in spite of focused research in this field for the past several decades, robust speech recognition with high reliability has not been achieved as it degrades in presence of speaker variabilities, channel mismatch condi- tions, and in noisy environments. The superb ability of the human auditory system has motivated researchers to include features of human perception in the speech recognition process. This dissertation investigates the roles of perceptual features of human hearing in automatic speech recognition in clean and noisy environments. Methods of simplified synaptic adaptation and two-tone suppression by companding are introduced by temporal processing of speech using a zero-crossing algorithm. It is observed that a high frequency enhancement technique such as synaptic adaptation performs better in stationary Gaussian white noise, whereas a low frequency enhancement technique such as the two-tone sup- pression performs better in non-Gaussian non-stationary noise types. The effects of static compression on ASR parametrization are investigated as observed in the psychoacoustic input/output (I/O) perception curves. A method of frequency dependent asymmetric compression technique, that is, higher compression in the higher frequency regions than the lower frequency regions, is proposed. By asymmetric compression, degradation of the spectral contrast of the low frequency formants due to the added compression is avoided. A novel feature extraction method for ASR based on the auditory processing in the cochlear nucleus is presented. The processings for synchrony detection, average discharge (mean rate) processing and the two tone suppression are segregated and processed separately at the feature extraction level according to the differential processing scheme as observed in the AVCN, PVCN and the DCN, respectively, of the cochlear nucleus. It is further observed that improved ASR performances can be achieved by separating the synchrony detection from the synaptic processing. A time-frequency perceptual spectral subtraction method based on several psychoacoustic properties of human audition is developed and evaluated by an ASR front-end. An auditory masking threshold is determined based on these psychoacoustic e?ects. It is observed that in speech recognition applications, spec- tral subtraction utilizing psychoacoustics may be used for improved performance in noisy conditions. The performance may be further improved if masking of noise by the tonal components is augmented by spectral subtraction in the masked region.