This paper presents an architecture for disparity estimation in real time which is designed to be used in a blind navigation assistance system. A highly pipelined hardware prototype has been designed and verified. Sum of Absolute Difference (SAD) algorithm is chosen as the cost function in the proposed architecture. The major design consideration is efficient hardware utilization and high throughput. This system is designed to support video resolutions upto 2048 x 2048 at high frame rates. The performance evaluation shows very low latency even at low processing frequency.
This work focuses on text dependent speaker verification system where a source feature specifically residual Mel frequency cepstral coefficients (RMFCC), has been extracted in addition to a vocal tract system feature namely Mel frequency cepstral coefficients (MFCC). The RMFCC features are derived from the LP residuals whereas MFCC features are derived from the cepstral analysis of the speech signal. Thus, these two features have different information about the speaker. A four cohort speaker's set has been prepared using these two features and dynamic time warping (DTW) is used as the classifier. Performance comparison of the text dependent speaker verification model using MFCC and RMFCC features are enumerated. Experimental results shows that, using RMFCC feature alone do not give satisfactory results in comparison to MFCC. Also, the system's performance obtained using the MFCC features, is not optimum. So, to improve the performance of the system, these two features are combined together using different combination algorithms. The proposed lowest ranking method yields good performance with an equal error rate (EER) of 7.50%. To further improve the efficiency of the system, the proposed method is combined along with the strength voting and weighted ranking method in the hierarchical combination method to obtain an EER of 3.75%.
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