Abstract: Systems, methods, and media for determining fraud risk from audio signals and non-audio data are provided herein. Some exemplary methods include receiving an audio signal and an associated audio signal identifier, receiving a fraud event identifier associated with a fraud event, determining a speaker model based on the received audio signal, determining a channel model based on a path of the received audio signal, using a server system, updating a fraudster channel database to include the determined channel model based on a comparison of the audio signal identifier and the fraud event identified, and updating a fraudster voice database to include the determined speaker model based on a comparison of the audio signal identifier and the fraud event identifier.

Abstract: Enhanced diarization systems and methods of use are provided herein. Some exemplary methods may include applying one or more rules that affect separation of the call audio data into segments, the rules being associated with the at least one call schema, separating the call audio data into segments according to the one or more rules, grouping segments of call audio data associated with a speaker, and storing in a storage media an identifier and the grouped segments for the speaker.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for disambiguating call data are provided herein. Some exemplary methods include receiving notification of a fraud event including a customer account identifier and a fraud time stamp; determining a time frame that is proximate the fraud time stamp; collecting call events associated with the customer account identifier that occur during the determined time frame, each call event including a unique call event identifier, a voice sample, and a call event time stamp; identifying a first call event belonging to a first speaker and a second call event belonging to a second speaker; and generating a timeline presentation that includes the first call event and call event timestamp and an identification of a first voice sample as belonging to the first speaker, the second call event and call event timestamp and an identification of a second voice sample as belonging to the second speaker.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for disambiguating call data are provided herein. Some exemplary methods include receiving, via a fraud notification system, notification of a fraud event associated with a customer account, the fraud event comprising a time stamp, determining, via a call selection module, unique voice samples or models from call events obtained within a time frame that is temporally proximate the fraud event, and generating a timeline presentation that includes each unique voice sample or model identified in the call events based upon a time stamp associated with the call events.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for generating fused risk scores for determining fraud in call data are provided herein. Some exemplary methods include generating a fused risk score used to determine fraud from call data by generating a fused risk score for a leg of call data, via a fuser module of an analysis system, the fused risk score being generated by fusing together two or more uniquely calculated fraud risk scores, each of the uniquely calculated fraud risk scores being generated by a sub-module of the analysis system; and storing the fused risk score in a storage device that is communicatively couplable with the fuser module.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: Systems, methods, and media for disambiguating call data are provided herein. Some exemplary methods include receiving, via a fraud notification system, notification of a fraud event associated with a customer account, the fraud event comprising a time stamp, determining, via a call selection module, unique voice samples or models from call events obtained within a time frame that is temporally proximate the fraud event, and generating a timeline presentation that includes each unique voice sample or model identified in the call events based upon a time stamp associated with the call events.

Abstract: Systems, methods, and media for determining fraud risk from audio signals and non-audio data are provided herein. Some exemplary methods include receiving an audio signal and an associated audio signal identifier, receiving a fraud event identifier associated with a fraud event, determining a speaker model based on the received audio signal, determining a channel model based on a path of the received audio signal, using a server system, updating a fraudster channel database to include the determined channel model based on a comparison of the audio signal identifier and the fraud event identified, and updating a fraudster voice database to include the determined speaker model based on a comparison of the audio signal identifier and the fraud event identifier.

Abstract: Systems, methods, and media for generating fused risk scores for determining fraud in call data are provided herein. Some exemplary methods include generating a fused risk score used to determine fraud from call data by generating a fused risk score for a leg of call data, via a fuser module of an analysis system, the fused risk score being generated by fusing together two or more uniquely calculated fraud risk scores, each of the uniquely calculated fraud risk scores being generated by a sub-module of the analysis system; and storing the fused risk score in a storage device that is communicatively couplable with the fuser module.

Abstract: Systems, methods, and media for analyzing fraud patterns and creating fraud behavioral models are provided herein. In some embodiments, methods for analyzing call data associated with fraudsters may include executing instructions stored in memory to compare the call data to a corpus of fraud data to determine one or more unique fraudsters associated with the call data, associate the call data with one or more unique fraudsters based upon the comparison, generate one or more voiceprints for each of the one or more identified unique fraudsters from the call data, and store the one or more voiceprints in a database.

Abstract: A system, method and computer program product are afforded for providing voice-enabled driving directions. Initially, an utterance representative of a destination address is received. Thereafter, the utterance is transcribed utilizing a speech recognition process. An origin address is then determined. A database is subsequently for queried generating driving directions based on the destination address and the origin address.

Abstract: A system, method and computer program product are afforded for providing voice-enabled driving directions. Initially, an utterance is received representative of a flight identifier. Utilizing a speech recognition process, the utterance is then transcribed. Further, a database is queried for generating flight information based on the flight identifier.

Abstract: A system, method and computer program product are afforded for providing localized content. Initially, an utterance representative of content is received from a user. Further, such utterance is transcribed utilizing a speech recognition process. A current location of the user is subsequently determined. Based on the transcribed utterance and the current location a database is queried for generating the content.

Abstract: A system, method and computer program product are afforded for providing voice-enabled driving directions. Initially, an utterance representative of a destination address is received. Thereafter, the utterance is transcribed utilizing a speech recognition process. An origin address is then determined. A database is subsequently for queried generating driving directions based on the destination address and the origin address.

Abstract: High level synthesis of datapaths has traditionally concentrated on synthesizing a specific implementation for a given computational problem. Methods to compose a reconfigurable BISR (built-in-self-repair) implementation with a minimum amount of area overhead are disclosed. Previously the BISR scope has been restricted to the substitution of operation modules with only those of the same type. Novel resource allocation, assignment and scheduling and transformation methods, primarily for ASIC designs, are described. These methods are based on the exploration of the design solution space accomplished by use of high level synthesis processes to find designs where resources of several different types can be backed up with the same unit.