A digital neural network approach to speech recognition

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents two novel methods for isolated word speech recognition based on sub-word components. A digital neural network is the fundamental processing strategy in both methods. The first design is based on the 'Separate Segmentation & Labelling' (SS&L) approach. The spectral data of the input utterance is first segmented into phoneme-like units which are then time normalised by linear time normalisation. The neural network labels the time-normalised phoneme-like segments 78.36% recognition accuracy is achieved for the phoneme-like unit. In the second design, no time normalisation is required. After segmentation, recognition is performed by classifying the data in a window as it is slid one frame at a time, from the start to the end of of each phoneme-like segment in the utterance. 73.97% recognition accuracy for the phoneme-like unit is achieved in this application. The parameters of the neural net have been optimised for maximum recognition performance. A segmentation strategy using the sum of the difference in filterbank channel energy over successive spectra produced 80.27% correct segmentation of isolated utterances into phoneme-like units. A linguistic processor based on that of Kashyap & Mittal [84] enables 93.11% and 93.49% word recognition accuracy to be achieved for the SS&L and 'Sliding Window' recognisers respectively. The linguistic processor has been redesigned to make it portable so that it can be easily applied to any phoneme based isolated word speech recogniser.This work is funded by the Ministry of Science & Technology, Government of Pakistan

Consequences of Political Instability, Governance and Bureaucratic Corruption on Inflation and Growth: The Case of Pakistan

This paper presents a theoretical model with micro-foundations that captures some important features of Pakistan's economy which have emerged in sixty-four years of its history. A comparison of Pakistan’s economic performance during different regimes shows that macroeconomic fundamentals tend to show an improvement during the autocratic regimes as compared with those prevailing during democratic regimes. In particular, periods of autocratic regimes are typically characterized by low inflation, robust growth and low level of bureaucratic corruption due to better governance. In contrast, the economic performance during the democratic regimes has been observed to worsen with weak governance and high levels of corruption, high inflation due partly to reliance on seigniorage to finance public spending, and lackluster growth. Using annual data from 1950 to 2011, computational modeling is carried out by applying Markov-Regime switching technique with maximum-likelihood procedures. The estimation results based on empirical modeling setup are supportive of the above stylized-facts and also confirm the implications of the theoretical model.Political Instability; Governance; Corruption; Inflation; Growth

An overview of Customer Relationship Management Software in Business Organizations

The aim of the article is to present details of the research on focus on the customer in relation to the utilization of the customer relationship management computerized system in business organization. Customer relationship management (CRM) plays a central role in the growth and success of corporations in an environment of fast technological development and the extensive competition currently in evidence. CRM empowers corporations with better customer awareness and helps to construct sustainable relationships with customers. People, technology, and processes are the three basic components of CRM. In order to ensure the successful implementation and adoption of the CRMinitiative, this paper presents a detailed review of the literature relating to CRM processes and its computerized system. Various types and levels of the CRM process as well as an understanding of the different perspectives of CRM are presented in this review. The paper suggests that it is important for an organization to understand the four major perspectives of CRM processes i.e. customer facing level processes, customer oriented processes, cross functional CRM processes and CRM macro-level processes. A survey is also conducted in this paper and a chi square test is performed to test the hypothesis relatingto the efficiency of customer relationship management and the client relationship it offers. Results show that the hypothesis is statistically significant

Choosing between endoscopic or microscopic removal of third ventricle colloid cysts

Colloid cysts are benign lesions, found in the anterior part of the roof of the third ventricle. A PubMED search of literature was performed to identify the evidence on different treatment options and surgical approaches for removal of colloid cysts. Evidence on endoscopic versus microsurgical resection of colloid cysts showed that microsurgical approach had significantly higher rates of gross total resection, lower recurrence rate and lower re-operation rate. No significant difference with respect to the mortality rate or the need for a shunt was found between the two groups. However, the overall morbidity rate was lower for the endoscopic group.

Margin Based Learning Framework with Geometric Margin Minimum Classification Error for Robust Speech Recognition

Statistical learning theorycombines empirical risk and generalization functionin single optimized objective function of margin based learning for optimization. Margin concept incorporating in Hidden Markov Model (HMM)for speech recognition, Margin based learning frame work based on minimum classification error (MCE) training criteria show higher capability over any other conventional DT methods in improvingclassification robustness (generalization capability) of the acoustic model by increasing the functional margin of the acoustic model. This paper introduces Geometric Margin based separation measure in the loss function definition of margin based learning frame work instead of functional margin separation measure to develop a mathematical framework of new optimize objective function of soft margin estimation (SME) for ASR. Derived SME objective function based on Geometric Margin based separation (misclassification) measure would be capable for representing the strength of margin based learning framework in term of classification robustness by minimizing the classification error probability as well asmaximizing the geometric margin

Pre-operative voriconazole in patients undergoing surgery for central nervous system fungal infections: Special report

Fungal infections of the central nervous system (CNS) are uncommon. Despite several advancements in diagnosis and treatment of these infections, the mortality rates remain high. The current retrospective study was planned to define the demographic and clinical features of patients with CNS fungal infections. Conducted at Aga Khan University Hospital, Karachi, and comprising CNS fungal infections operated between January 2000 and December 2015. The study analysed whether a short course of pre-operative anti-fungal therapy may improve outcomes in these patients. There were 47 cases confirmed on histopathology and/or microbiology. Outcome measures used were Glasgow coma score (GCS), Glasgow outcome score (GOS) and Karnofsky performance score (KPS). The overall 30-day mortality was 20(42.5%). Fungal infections of the CNS can occur in both immune-compromised and immune-competent patients. Early diagnosis, radical surgery, pre-operative anti-fungal therapy for at least 2 weeks, pre- and postoperative Voriconazole therapy results in more favourable outcomes

Investigation of Flow Dynamics Around a Combination of Different Head Shapes of Spur Dikes

Spur dikes are widely used as river training structures throughout the globe to improve navigation, strengthen flood protection, and save erodible banks. This study investigates the flow behaviour of multiple spurs using similar and different head shapes instead of adding an extra structure. The novelty of the study lies in finding out the best combination of head shapes among circular (C), rectangular (R), and triangular (T) that can reduce the responsible factors of scouring and erosion. The responsible factors for scour and erosion include high magnitude velocity, pressure, turbulence kinetic energy (TKE), Reynold stresses, and wall shear stresses. Nine combinations (3 same, i.e., CCC, RRR, and TTT and six different, i.e., CRC, CTC, RCR, RTR, TCT, TRT) of spurs were investigated using Computational Fluid Dynamics (CFD) code FLUENT. Firstly, in the analysis of similar head shapes, more reduction in the values of scour and erosion responsible factors were observed in CCC combination (20% in velocity, 45% in pressure, 41% in TKE, and 43% in normal Reynold stresses). Finally, the reduction was further improved in analysing different head shapes. The CTC combination showed the most effective results in reducing the prescribed factors (43% in velocity, 57% in pressure, 51% in TKE, and 54% in normal Reynold stresses) compared to both combinations of head shapes. Therefore, to protect riverbank and spur head failure due to severe turbulent flow, the combination of spurs (CTC) could be preferred

Quality enhancement at higher education institutions by early identifying students at risk using data mining

Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses

Monetary policy, informality and business cycle fluctuations in a developing economy vulnerable to external shocks

This paper develops an open economy dynamic stochastic general equilibrium (DSGE) model based on New-Keynesian micro-foundations. Alongside standard features of emerging economies, such as a combination of producer and local currency pricing for exporters, foreign capital inflow in terms of foreign direct investment and oil imports, this model also incorporates informal labor and production sectors. This customization intensifies the exposure of a developing economy to internal and external shocks in a manner consistent with the stylized facts of Business Cycle Fluctuations. We then focus on optimal monetary policy analysis by evaluating alternative interest rate rules and calibrate the model using data from Pakistan economy. The learning and determinacy analysis suggest monetary authority in developing economies to follow Taylor principle in large and to put some weight on exchange rate fluctuations even if there is relatively less inertia in the setting of policy interest rate

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field