Abstract: A system and method for determining and authenticating a person's identity by generating a behavioral profile for that person by presenting that person with various stimulus and measuring that person's response characteristics in an enrollment stage. That person's response profile, once generated is stored. When that user subsequently needs to access a secure resource, that user to be authorized is presented with the stimulus that was presented at the time of generating that person's behavioral profile and the person's responses are detected and compared to his/her behavioral profile. If a match is detected, that user is identified. The user's behavioral response may be in the form of signals as detected by sensor means that detects visual or audible emotional cues or as signals resulting from that person's behavior as detected by polygraph or EEG devices.

Abstract: Methods and apparatus for providing speech-based authentication, including the determination of a target discriminant based on an identity claim and on at least one target voiceprint model relating to a target speaker, of a background discriminant based on the identity claim and on at least one background voiceprint model relating to at least one background speaker, and of a score based on the target discriminant and the background discriminant, which score is used to accept or reject the identity claim.

Abstract: Methods and apparatus are provided for sequential authentication of a user that employ one or more error rates characterizing each security challenge. According to one aspect of the invention, a user is challenged with at least one knowledge challenge to obtain an intermediate authentication result; and the user challenges continue until a cumulative authentication result satisfies one or more criteria. The intermediate authentication result is based, for example, on one or more of false accept and false reject error probabilities for each knowledge challenge. A false accept error probability describes a probability of a different user answering the knowledge challenge correctly. A false reject error probability describes a probability of a genuine user not answering the knowledge challenge correctly. The false accept and false reject error probabilities can be adapted based on field data or known information about a given challenge.

Abstract: There is provided an apparatus for providing a Text Independent (TI) speaker recognition mode in a Text Dependent (TD) Hidden Markov Model (HMM) speaker recognition system and/or a Text Constrained (TC) HMM speaker recognition system. The apparatus includes a Gaussian Mixture Model (GMM) generator and a Gaussian weight normalizer. The GMM generator is for creating a GMM by pooling Gaussians from a plurality of HMM states. The Gaussian weight normalizer is for normalizing Gaussian weights with respect to the plurality of HMM states.

Abstract: Methods and arrangements for facilitating speaker identification. At least one N-best list is generated based on input speech, a system output is posited based on the input speech, and a determination is made, via at least one property of the N-best list, as to whether the posited system output is inconclusive.

Abstract: Methods and apparatus for verifying spoken passwords and sentences with small decoder complexity requirements. A proposed method permits the verbal verification of a spoken password sentence without computationally extensive large-vocabulary decoding. A decoder preferably uses target baseforms (representing the original content to be verified) together with a special set of competing simplified baseforms that may be easily constructed using finite-state grammars (FSG).

Abstract: In large-scale deployments of speaker recognition systems the potential for legacy problems increases as the evolving technology may require configuration changes in the system thus invalidating already existing user voice accounts. Unless the entire database of original speech waveform were stored, users need to reenroll to keep their accounts functional, which, however, may be expensive and commercially not acceptable. Model migration is defined as a conversion of obsolete models to new-configuration models without additional data and waveform requirements. The present disclosure investigates ways to achieve such a migration with minimum loss of system accuracy.

Abstract: Methods and arrangements for assessing the identity of an individual. Input is accepted from an individual, and at least one user group is attributed to the individual. This attributing is repeated until the identity of the individual is assessed.

Abstract: In automatic pattern recognition, in the context of patterns being observed either in the same or a new environment, e.g. a new acoustic channel, as compared to the one seen during the previous enrollment, an improvement wherein degradation of the system recognition accuracy caused by environment/channel mismatches is averted.

Abstract: A system and method for determining and authenticating a person's identity by generating a behavioral profile for that person by presenting that person with various stimulus and measuring that person's response characteristics in an enrollment stage. That person's response profile, once generated is stored. When that user subsequently needs to access a secure resource, that user to be authorized is presented with the stimulus that was presented at the time of generating that person's behavioral profile and the person's responses are detected and compared to his/her behavioral profile. If a match is detected, that user is identified. The user's behavioral response may be in the form of signals as detected by sensor means that detects visual or audible emotional cues or as signals resulting from that person's behavior as detected by polygraph or EEG devices.

Abstract: A method, computer program product, and data processing system for compensating for fundamental frequency changes in a frame-based speech processing system is disclosed. In a preferred embodiment of the present invention, a frame of a voiced speech signal is processed by an inverse linear-predictive filter to obtain a residual signal that is indicative of the fundamental tone emitted by the speaker's vocal cords. A transformation function is applied to the frame to limit the frame to an integer number of pitch cycles. This transformed frame is used in conjunction with vocal tract parameters obtained from the original speech signal frame to construct a pitch-adjusted speech signal that can more easily be understood by speech- or speaker recognition software.

Abstract: Methods and apparatus for facilitating speaker recognition, wherein, from target data that is provided relating to a target speaker and background data that is provided relating to at least one background speaker, a set of cohort data is selected from the background data that has at least one proximate characteristic with respect to the target data. The target data and the cohort data are then combined in a manner to produce at least one new cohort model for use in subsequent speaker verification. Similar methods and apparatus are contemplated for non-voice-based applications, such as verification through fingerprints.

Abstract: Methods and apparatus are disclosed for detecting non-target language references in an audio transcription or speech recognition system using a confidence score. The confidence score may be based on (i) a probabilistic engine score provided by a speech recognition system, (ii) additional scores based on background models, or (iii) a combination of the foregoing. The engine score provided by the speech recognition system for a given input speech utterance reflects the degree of acoustic and linguistic match of the utterance with the trained target language. The background models are created or trained based on speech data in other languages, which may or may not include the target language itself. A number of types of background language models may be employed for each modeled language, including one or more of (i) prosodic models; (ii) acoustic models; (iii) phonotactic models; and (iv) keyword spotting models.

Abstract: In detection systems, such as speaker verification systems, for a given operating point range, with an associated detection “cost”, the detection cost is preferably reduced by essentially trading off the system error in the area of interest with areas essentially “outside” that interest. Among the advantages achieved thereby are higher optimization gain and better generalization. From a measurable Detection Error Tradeoff (DET) curve of the given detection system, a criterion is preferably derived, such that its minimization provably leads to detection cost reduction in the area of interest. The criterion allows for selective access to the slope and offset of the DET curve (a line in case of normally distributed detection scores, a curve approximated by mixture of Gaussians in case of other distributions). By modifying the slope of the DET curve, the behavior of the detection system is changed favorably with respect to the given area of interest.

Abstract: Methods and arrangements for facilitating data clustering. From a set of input data, a predetermined number of non-overlapping subsets are created. The input data is split recursively to create the subsets.

Abstract: Methods and arrangements for facilitating speaker identification. At least one N-best list is generated based on input speech, a system output is posited based on the input speech, and a determination is made, via at least one property of the N-best list, as to whether the posited system output is inconclusive.

Abstract: Methods and apparatus for the rapid adaptation of classification systems using small amounts of adaptation data. Improvements in classification accuracy are attainable when conditions similar to those that present in adaptation are observed. The attendant methods and apparatus are suitable for a wide variety of different classification schemes, including, e.g., speaker identification and speaker verification.