The invention provides a method and system for classifying insurance files for identification, sorting and efficient collection of subrogation claims. The invention determines whether an insurance claim has merit to warrant claim recovery efforts utilizing software code for partially describing a set of documents having unstructured and structured file data containing terms and phrases having contextual bases, code for transforming the terms and phrases, code for iterating a classification process to determine rules that best classify the set of documents based upon context, code for incorporating the rules into an induction and knowledge representation, thesauri taxonomies and text summarization to classify subrogation claims; code for calculating a base score and a concept vector to identify the selected claims that demonstrate a given probability of subrogation recovery.

Method and system for classifying documents

An online biometric verification system for use over the Internet and requiring no specialist equipment is presented. Combining two distinct tests to ensure authenticity, a typing style test and a mouse-based signature test, achieves a fraudulent access rate of /spl ap/ 4.4 percent, while authentic users access with a rate of /spl ap/ 99 percent.

Java-based Internet biometric authentication system

Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks

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.

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A New Speaker Recognition System with Combined Feature Extraction Techniques

Acoustic characteristics have played an essential role in biometrics. In this article, we introduce a robust, text-independent speaker identification/verification system. This system is mainly based on a subspace-based enhancement technique and probabilistic support vector machines (SVMs). First, a perceptual filterbank is created from a psycho-acoustic model into which the subspace-based enhancement technique is incorporated. We use the prior SNR of each subband within the perceptual filterbank to decide the estimator's gain to effectively suppress environmental background noises. Then, probabilistic SVMs identify or verify the speaker from the enhanced speech. The superiority of the proposed system has been demonstrated by twenty speaker data taken from AURORA-2 database with added background noises

Robust Speaker Identification and Verification

Speaker identification may be employed as part of a security system requiring user authentication. In this case, the claimed identity of the user is known from a magnetic card and PIN number, for example, and an utterance is requested to confirm the identity of the user. A fast response is necessary in the confirmation phase and a fast registration process for new users is desirable. The time encoded signal processing and recognition (TESPAR) digital language is used to preprocess the speech signal. A speaker cannot be identified directly from the single TESPAR vector since there is a highly nonlinear relationship between the vector's components such that vectors are not linearly separable. Therefore the vector and its characteristics suggest that classification using a neural network will provide an effective solution. Good classification performance has been achieved using a probabilistic RAM (pRAM) neuron. Four probabilistic pRAM neural network architectures are presented. A performance of approximately 97% correct classifications has been obtained, which is similar to results obtained elsewhere (M. Sharma and R.J. Mammone, 1996), and slightly better than a MLP network. No speech recognition stage was used in obtaining these results, so the performance relates only to identifying a speaker's voice and is therefore independent of the spoken phrase. This has been achieved in a hardware-realizable system which may be incorporated into a smart-card or similar application.

Yelin Guan

Papers

Speaker identification for security systems using reinforcement-trained pRAM neural network architectures