Using a proper distribution function for speech signal or for its representations is of crucial importance in statisticalbased speech processing algorithms. Although the most commonly used probability density function (pdf) for speech signals is Gaussian, recent studies have shown the superiority of super-Gaussian pdfs. A large research effort has focused on the investigation of a univariate case of speech signal distribution; however, in this paper, we study the multivariate distributions of speech signal and its representations using the conventional distribution functions, e.g., multivariate Gaussian and multivariate Laplace, and the copula-based multivariate distributions as candidates. The copula-based technique is a powerful method in modeling non-Gaussian multivariate distributions with non-linear inter-dimensional dependency. The level of similarity between the candidate pdfs and the real speech pdf in different domains is evaluated using the energy goodness-of-fit test. In our evaluations, the best-fitted distributions for speech signal vectors with different lengths in various domains are determined. A similar experiment is performed for different classes of English phonemes (fricatives, nasals, stops, vowels, and semivowel/glides). The evaluation results demonstrate that the multivariate distribution of speech signals in different domains is mostly super-Gaussian, except for Mel-frequency cepstral coefficient. Also, the results confirm that the distribution of the different phoneme classes is better statistically modeled by a mixture of Gaussian and Laplace pdfs. The copula-based distributions provide better statistical modeling of vectors representing discrete Fourier transform (DFT) amplitude of speech vectors with a length shorter than 500 ms.
Background: Human resources in health are limited, and a variety of healthcare services are increasing, especially in developing countries. This study aimed to determine the anesthesia and surgical technologist staff for operating room based on the Workload Indicators of Staffing Need (WISN).Methods: This cross-sectional descriptive study was conducted at Shahid Madani Medical and Training Hospital in 2019. The data collected through interview and hospital documents observation. The WISN method was used to analyze and determine staffing need.Results: The WISN ratio for anesthesia and surgical technologist staff were 1.60 and 4.94, respectively. In other words, in both categories, this ratio is >1.00. Conclusions: WISN results can used to assess staffing capacity and capability over the previous year and the policy and financial implications due to staff shortages or surplus. As the workload of staff is an important issue and the staff have a right to have a standard workload in their position, then determining this workload is an essential issue in healthcare organization. Also, WISN method work as a human resource management tools, it improvers health managers to make right decision about staffing needs and then better manage of organization’s resources.
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