Sharif Digital Repository

Automatic speech recognition performance degrades significantly when speech is affected by environmental noise. Nowadays, the major challenge is to achieve good robustness in adverse noisy conditions so that automatic speech recognizers can be used in real situations. Spectral subtraction (SS) is a well-known and effective approach; it was originally designed for improving the quality of speech signal judged by human listeners. SS techniques usually improve the quality and intelligibility of speech signal while speech recognition systems need compensation techniques to reduce mismatch between noisy speech features and clean trained acoustic model. Nevertheless, correlation can be expected... 

Spectral Subtraction (SS), as a speech enhancement technique, originally designed for improving quality of speech signal judged by human listeners. it usually improve the quality and intelligibility of speech signals, while the speech recognition systems need compensation techniques capable of reducing the mismatch between the noisy speech features and the clean models. This paper proposes a novel approach for solving this problem by considering the SS and the speech recognizer as two interconnected components, sharing the common goal of improved speech recognition accuracy. The experimental evaluations on a real recorded database and the TIMIT database show that the proposed method can... 

This paper has presented a novel discriminative parameters calibration approach based on the Model Distance Maximizing (MDM) to improve the performance of our previous proposed robustness method named spectral subtraction (SS) in likelihoodmaximizing framework. In the previous work, for adjusting the spectral over-subtraction factor of SS, conventional ML approach is used that only utilizes the true model without considering other confused models. This makes it very probably to reach a suboptimal solution. While in MDM, by maximizing the dissimilarities among models, the performance of our speech recognizer-based spectral subtraction method could be further improved. Experimental results... 

Side Channel Analysis (SCA) are still harmful threats against security of embedded systems. Due to the fact that every kind of SCA attack or countermeasure against it needs to be implemented before evaluation, a huge amount of time and cost of this process is paid for providing high resolution measurement tools, calibrating them and also implementation of proposed design on ASIC or target platform. In this paper, we have introduced a novel simulation platform for evaluation of power based SCA attacks and countermeasures. We have used Synopsys power analysis tools in order to simulate a processor and implement a successful Differential Power Analysis (DPA) attack on it. Then we focused on the... 

Face recognition has been an important topic in computer vision for the last two decades. While many algorithms have been developed to address this issue, one of the major challenges faced by them is variation in pose. One of the possible solutions is to find invariant features among different poses of a single person. In this paper a geometry mapping between a frontal face and its rotated pose is used to find invariant features for pose robust face recognition. This mapping is solely based on the angle of rotation and indicates mutual regions between a frontal view of a person and its rotated image. Invariant features based on the low frequency coefficients of these mutual regions are then...