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Problem statement:This study introduces a new method for speaker verification system by fusing two different feature extraction methods to improve the recognition accuracy and security. Approach: The proposed system uses Mel frequency cepstral coefficients for speaker identification and Modified MFCC for verification. For speaker modeling vector quantization is used. Results: The proposed system was investigated the effect of the different length segmental feature as well as speaker modeling for speaker recognition. The performance was evaluated against 1000 speakers for 10 different languages with duration of 10 sec for training the system and for testing 5 sec. duration samples were used. Conclusion/Recommendations: Experimental results of the proposed system showed that higher recognition accuracy of 93% is achieved by increasing the number of filter banks used for feature extraction method, more competitive with existing system using vector quantization with lesser computational complexity. The system efficiency may further be improved using other speaker modeling techniques like GMM, HMM.

Problem statement: The received voice signal in mobile communication is often disturbed by background noise and hence there is a need for good noise reduction methods for enhancing Speech. It is well known that denoising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To address this issue, a new method for enhancing speech from background interference is presented in this study by fusing dual band spectral subtraction with adaptive noise estimator and wavelet packet based thresholding method. Approach: The proposed system uses the combination of dual band Spectral Subtraction method with adaptive noise estimator for pre-processing, in order to initially reduce the noise level and further the quality of speech is improved by Wavelet Packet Transform (WPT) based level dependent thresholding method. The threshold value is determined by using Steins Unbiased Risk Estimator (SURE) and hard, soft, Garrotte, µ-law and a proposed modified soft thresholding functions are considered for denoising. Results: The proposed method was investigated by ten different clean speech samples (five male and five female) taken from TIMIT database and thirteen different noise sources to degrade the speech artificially and the energy level of the noise is scaled such that the overall SNR of the noisy speech is maintained at -5, 0,5,10 and 15 dB and finally the results are evaluated using objective and subjective measures. Conclusion/Recommendations: It is suggested from the experimental results that the proposed scheme gives improved spectral performance, reflects in better speech quality in all types of noisy environment. For better speech enhancement in noise dominated regions, the system efficiency is further improved by fusing threshold values for wavelet denoising

Automatic identification of facial expression is a significant research area which is anticipated for real time processing in Human-Computer Interaction domain. Along with an efficient classifier for assigning the class label to each of the input face image, it is very necessary to have a strong feature vector for training the classifier. This paper proposes an effectual combination of Local Binary Pattern and Symbolic Aggregate approXimation method for the feature vector generation for the classifier. Twenty one facial patches are extracted from the face image and the LBP value and SAX string for these twenty one patches are utilised for feature vector generation. The feature vectors of images are submitted to the Ensemble Bag classifier for training purpose. Images which were not used for training is used for testing. An average accuracy of 98.7% was obtained when tested on JAFFE data set for seven expressions and an accuracy of 96.96% was obtained for nine expressions on fused database. A detailed analysis of the testing conducted on images with partial occlusion and illumination variance are presented here.

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