Merit, Speech Disability and Discrimination

There is a substantial body of evidence – going back over decades – which indicates that the employment sphere is difficult for those who suffer a speech disability. To a large extent, I argue, this is due to the setting of merit in terms of orality and aesthetic. It also relates to the low perception of competence of the speech disabled. I argue that to be effective against discrimination the notion of merit and its assessment requires focus. ‘Merit’ as a concept in discrimination law has had its critics, yet it remains important to investigate it as social construct in order to help understand discrimination and how to counter this. For example, in this article I look at an instance where the resetting of what was viewed as ‘meritorious’ in judicial recruitment successfully improved the diversity in lower judicial posts.

Further, given the relative failure of the employment tribunal system to improve the general position of those who are disabled, I look to alternative methods of countering disability discrimination. The suggestion provided is that an enforced ombudsman type approach capable of dealing with what may be the core issue around employment discrimination (‘merit’) would provide a better mechanism for handling the general situation of disability discrimination than the tribunal system.

Wide Matching - An Approach to Improving Noise Robustness for Speech Enhancement

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to evaluate the new approach with variable noises and SNRs from -5 dB to noise free. It is shown that the new approach, without any estimation of the noise, significantly outperformed conventional methods in the low SNR conditions while retaining comparable performance in the high SNR conditions. It is further suggested that the wide matching and deep learning approaches can be combined towards a highly robust and accurate speech estimator.