Information technology (IT) outsourcing has been a business practice for more than two decades. Researchers have suggested successful risk management as a key factor in successful IT outsourcing projects implementation. The documented investigations, however, have mainly addressed risk management only from a single perspective of either clients or IT vendors. Considering only one perspective allows for an omission of possible risks considered critical by the other party, as suggested by agency theory. This study explored the potential perception inconsistency regarding the risks between the client and the vendor for IT outsourcing projects by using a quasi‐Delphi approach. The analysis results indicated some inconsistencies in the risks perceived by the two parties: (1) the clients regarded (a) lack of vendor commitment to the project and (b) poor vendor selection criteria and process as top critical risks but the vendors didn't; and (2) on the other hand, the vendors perceived (a) unclear requirements and (b) lack of experience and expertise with project activities as significant risks but the clients didn't. Insights into how the client and the vendor perceive risks may help both parties determine how to partner and manage project risks collaboratively to succeed in outsourcing.
In many applications, speech recognition must operate in conditions where there are some distances between speakers and the microphones. This is called distant speech recognition (DSR). In this condition, speech recognition must deal with reverberation. Nowadays, deep learning technologies are becoming the the main technologies for speech recognition. Deep Neural Network (DNN) in hybrid with Hidden Markov Model (HMM) is the commonly used architecture. However, this system is still not robust against reverberation. Previous studies use Convolutional Neural Networks (CNN), which is a variation of neural network, to improve the robustness of speech recognition against noise. CNN has the properties of pooling which is used to find local correlation between neighboring dimensions in the features. With this property, CNN could be used as feature learning emphasizing the information on neighboring frames. In this study we use CNN to deal with reverberation. We also propose to use feature transformation techniques: linear discriminat analysis (LDA) and maximum likelihood linear transformation (MLLT), on mel frequency cepstral coefficient (MFCC) before feeding them to CNN. We argue that transforming features could produce more discriminative features for CNN, and hence improve the robustness of speech recognition against reverberation. Our evaluations on Meeting Recorder Digits (MRD) subset of Aurora-5 database confirm that the use of LDA and MLLT transformations improve the robustness of speech recognition. It is better by 20% relative error reduction on compared to a standard DNN based speech recognition using the same number of hidden layers.
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