Knuckling Under? School Superintendents and Accountability-Based Educational Reform

The goal of this article is to explore the various ways that superintendents have responded to accountability-based educational reform efforts such as No Child Left Behind, the factors that have influenced their responses, and the implications of these responses for current and future educational leaders. With respect to the first issue, empirical data from a number of nationai studies (T. E. Glass & Franceschini, 2007; Johnson, Arumi, & Ott, 2006; Johnstone, Dilkkers, & Luedeke, 2009; Stecher et al., 2008) make clear that while there have been a variety of responses from superintendents to accountability-based reform efforts, superintendents have mostly played a supportive role. Examining the situation more fully suggests that the driving factors behind superintendent support for accountability-based educational reform are complex and are often deeply embedded within the "DNA" of the role of superintendent. This article examines the structure of this DNA by looking at the factors that influence superintendents' views of accountability-based educational reform from historical, political, and institutional perspectives. This muitifaceted approach provides new insights into the complex relationship that exists between the structure of the role of superintendent and the agency of the individuals who inhabit that role.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.