Abstract: The fields of biometrics and commerce can benefit from the aggressive compression techniques presented here. A novel approach to compressing the on-line handwriting signal without losing the dynamic information (i.e., velocity and order) is presented which provides the means to fit almost any signature sample within the 57-byte limit of one of the free magnetic strips on the back of conventional transaction cards such as credit or debit cards or other identity cards. The apparatus requires an on-line signature capture tablet capable of sampling the handwritten signature in a series of sampled X and Y coordinates. The compression method starts with segmenting the on-line handwriting signal into portions which may be approximated with a set of features from which the velocity and position may be reconstructed. These features are then compressed further using standard compression techniques. A Huffman coding technique is used to demonstrate this step.

Abstract: In an illustrative embodiment, a speaker model is generated for each of a number of speakers from which speech samples have been obtained. Each speaker model contains a collection of distributions of audio feature data derived from the speech sample of the associated speaker. A hierarchical speaker model tree is created by merging similar speaker models on a layer by layer basis. Each time two or more speaker models are merged, a corresponding parent speaker model is created in the next higher layer of the tree. The tree is useful in applications such as speaker verification and speaker identification.

Abstract: A method for generating a hierarchical speaker model tree. In an illustrative embodiment, a speaker model is generated for each of a number of speakers from which speech samples have been obtained. Each speaker model contains a collection of distributions of audio feature data derived from the speech sample of the associated speaker. The hierarchical speaker model tree is created by merging similar speaker models on a layer by layer basis. Each time two or more speaker models are merged, a corresponding parent speaker model is created in the next higher layer of the tree. The tree is useful in applications such as speaker verification and speaker identification. A speaker verification method is disclosed in which a claimed ID from a claimant is received, where the claimed ID represents a speaker corresponding to a particular one of the speaker models. A cohort set of similar speaker models associated with the particular speaker model is established.

Abstract: A method for computing a distance between collections of distributions or finite mixture models of features. Data is processed so as to define at least first and second collections of distributions of features. For each distribution of the first collection, the distance to each distribution of the second collection is measured to determine which distribution of the second collection is the closest (most similar). The same procedure is performed for the distributions of the second collection. Based on the closest distance measures, a final distance is computed representing the distance between the first and second collections. This final distance may be a weighted sum of the closest distances. The distance measure may be used in a number of applications such as [speaker classification,] speaker recognition and audio segmentation.

Abstract: Methods and apparatus for performing speaker recognition comprise processing a video signal associated with an arbitrary content video source and processing an audio signal associated with the video signal. Then, an identification and/or verification decision is made based on the processed audio signal and the processed video signal. Various decision making embodiments may be employed including, but not limited to, a score combination approach, a feature combination approach, and a re-scoring approach. In another aspect of the invention, a method of verifying a speech utterance comprises processing a video signal associated with a video source and processing an audio signal associated with the video signal. Then, the processed audio signal is compared with the processed video signal to determine a level of correlation between the signals. This is referred to as unsupervised utterance verification.