A Comparative Survey on Biometric Identity Authentication Techniques in .NET

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A Comparative Survey on Biometric Identity Authentication Techniques
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were trained with some 500,000 feature pairs from 20 speakers and tested with the remaining 10 speakers along with the 20 speakers. The authors claim that the above-presented method showed good generalization and is virtually speaker-independent. They further claim that new speakers do not require a retraining of the ANNs. But further details on how generalization is achieved are not provided. Also, the training set used is very small and if thousands of users are involved, generalization may not hold good.
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3.2.2 Speaker Specific Mapping for Text-Independent Speaker Recognition
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In reference 2, the authors propose a mapping approach for the task of textindependent speaker recognition. The mapping property of a multilayer feed-forward neural network (MLFFNN) is used to generate a model for each speaker. In the mapping approach, speaker-speci c information is captured by mapping a set of parameter vectors speci c to linguistic information in the speech to a set of parameter vectors having linguistic and speaker information. Linear prediction (LP) coding-derived cepstral coef cients are used to derive suitable vectors for mapping approach. LP analysis is used to obtain clues about parameters that contain predominantly linguistic information or linguistic and speaker information. After selecting the parameter vectors suitable for mapping, the next task is to derive the mapping function itself. The nonlinear speaker-speci c mapping function can be captured using the MLFFNN, where the mean-squared error is minimized using a gradient descent algorithm. For testing, the input parameter vector is presented to each MLFFNN, and the difference between the desired output vector and the actual output vector of the MLFFNN is used as a distance for that frame. The total accumulated distance is then averaged over all test frames to give an indication of the proximity of test utterance. Euclidean distance between the output of the network and the desired output parameter vector was used for evaluating the performance of a speaker model relative to the models of other speakers. A background model (BG) is generated using the parameter vectors extracted from speech utterances of a large number of speakers registered with the system. The MLFFNN is trained with the pooled input and output parameter vectors from all the speakers. These weights from the BG model are then used to train each speaker model. This avoids the bias that any arbitrary initial weights may introduce while generating a speaker model. The relative score for the test signal is obtained using the difference between the average distance for the BG model and the speaker model. Also, investigation on the frequency content shows that speaker-speci c information is available in the higher frequencies. The authors claim that the proposed mapping approach performs as ef ciently as the GMM-based approaches for all the 630 speakers in the database. But the number of free parameters is much less as compared to the Gaussian Mixture Models-based approach.
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3.2 Voice Biometric
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3.2.3 Neural Network for Improved Text-Independent Speaker Identification
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In reference 3, the authors present a neural network array (NNA) that combines a binary partitioned approach having pattern index information with decision trees. The authors verify that the NNA can not only reduce the computation cost of training and recognition, but also reduce the classi cation error. Speaker identi cation with radial basis function neural network array (RBFNNA) is considered as an application of the NNA. The advantage of using NNA is that the architecture is expandable when a new entry is added; the main disadvantage of NNs needing to retain the entire network for the new catalog is partly overcome by the NNA. The authors present a fast searching algorithm for distinguishing neural networks (NN) catalogs by a cascading a decision and pruning criteria. The subnet is trained by two catalogs. In recognition stage, a subnet could accept one catalog and reject the other catalog. If a catalog is accepted, we consider that it was similar to the correct catalog and the other must be incorrect. The search path for the algorithm is from the top row to the bottom row. All the subnets that reject the unknown catalog will be pruned. The database used consisted of speech utterances of Chinese words, by 20 male postgraduate students under normal lab conditions. The speech signal was sampled at 8 kHz with 16 bits ADC. Sixteen orders of linear predictive cepstral coef cients (LPCC) of each frame is adopted as the speaker s features. The frames consist of 256 sample points with 128 overlapping sample points. The authors use the RBFNN because it has the same underlying structure of the Gaussian mixture models usually used in similar applications and also because the RBFNNs have ef cient training algorithms where the number of nodes in the hidden layer can be automatically determined by orthogonal least squares. The number of nodes in the rst layer is 16, in the output layer it is 1, and in the hidden layer it is automatically determined. The authors conclude that the larger the number of speakers, the higher the error rate for identi cation. To reduce the error rate, additional information of the catalog index can be used. Although NNA could deal with any classifying problem, its application for speaker identi cation is limited. The authors conclude that, additionally, the NNAs are more suitable for dif cult tasks like automatic classi cation of EEG or biomedical signals where the signals are corrupted to a higher degree.
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