Doctor of Philosophy
Electrical and Computer Engineering
Dr. Vijay Parsa
Speech identification in the presence of background noise is difficult for children with auditory processing disorder and adults with sensorineural hearing loss. The listening difficulty arises from deficits in their temporal, spectral, binaural, and/ or cognitive processing. Given the lack of improvement with conventional assistive hearing devices, alternate speech processing methodologies, which exaggerate the temporal and spectral cues, need to be developed to improve speech intelligibility for individuals who have poor temporal and/ or spectral processing.
This thesis first, reports results from a series of experiments on subjective and objective assessments of two different schemes of envelope enhancement algorithms (dynamic and static) across different types and levels of background noise. The subjective results revealed that the speech intelligibility scores are lower for children with auditory processing disorder compared to children with normal hearing. The subjective results also demonstrated that enhancing the temporal envelope is much more beneficial for children with auditory processing disorder when compared to children with normal hearing. Comprehensive objective assessments, which were conducted by developing novel intrusive and non-intrusive objective speech intelligibility predictors, demonstrated that both dynamic and static envelope enhancement algorithms are only effective in improving speech intelligibility under certain processing conditions that depended on the type, level and location of the background noise. Furthermore, the application of noise reduction algorithms prior to the envelope enhancement techniques increased their range of effectiveness. Second, using the proposed objective predictors, the effectiveness of a companding architecture (which enhances both temporal and spectral cues) is shown to be better than temporal envelope enhancement alone, across different noisy environments in the presence of a noise reduction algorithm.
Third, the application of the binaural dichotic processing is evaluated in stationary and non-stationary background noise environments through subjective experiments. The subjective results demonstrated that the dichotic processing is mainly effective in improving speech intelligibility for stationary background noise at poor signal to noise ratios. It is also shown that the incorporation of a noise reduction algorithm as a front-end to the dichotic hearing processing is inferior to increase its range of effectiveness regardless of the type and level of the background noise.
Moshgelani, Farid, "Development and Assessment of Signal Processing Algorithms for Assistive Hearing Devices" (2019). Electronic Thesis and Dissertation Repository. 6139.