Abstract: Embodiments described herein provide for a machine-learning architecture for modeling quality measures for enrollment signals. Modeling these enrollment signals enables the machine-learning architecture to identify deviations from expected or ideal enrollment signal in future test phase calls. These differences can be used to generate quality measures for the various audio descriptors or characteristics of audio signals. The quality measures can then be fused at the score-level with the speaker recognition's embedding comparisons for verifying the speaker. Fusing the quality measures with the similarity scoring essentially calibrates the speaker recognition's outputs based on the realities of what is actually expected for the enrolled caller and what was actually observed for the current inbound caller.

Abstract: Embodiments described herein provide for systems and methods for voice-based cross-channel enrollment and authentication. The systems control for and mitigate against variations in audio signals received across any number of communications channels by training and employing a neural network architecture comprising a speaker verification neural network and a bandwidth expansion neural network. The bandwidth expansion neural network is trained on narrowband audio signals to produce and generate estimated wideband audio signals corresponding to the narrowband audio signals. These estimated wideband audio signals may be fed into one or more downstream applications, such as the speaker verification neural network or embedding extraction neural network. The speaker verification neural network can then compare and score inbound embeddings for a current call against enrolled embeddings, regardless of the channel used to receive the inbound signal or enrollment signal.

Abstract: Embodiments disclosed herein include software processes executed by a computer for encoding and decoding watermarks for a speech signal in a call signal communicated via telephony channels. An encoder uses Linear Predictive Coding (LPC) to analyzes the call signal's spectral envelope and embeds the watermark into the LPC log-spectrum of the speech signal of the call signal. The encoder may reduce the watermark's strength at a formant peak of the speech signal, balancing the watermark's robustness and detectability. A deep decoder includes a neural network architecture trained on watermarked and watermark-free speech signals having various types of degradation to extract a feature vector of a call signal and compute a watermark detection score for one or more frames or for the call signal. At inference time, the deep decoder detects the watermark when the watermark detection score satisfies a detection threshold.

Abstract: A method of obtaining and automatically providing secure authentication information includes registering a client device over a data line, storing information and a changeable value for authentication in subsequent telephone-only transactions. In the subsequent transactions, a telephone call placed from the client device to an interactive voice response server is intercepted and modified to include dialing of a delay and at least a passcode, the passcode being based on the unique information and the changeable value, where the changeable value is updated for every call session. The interactive voice response server forwards the passcode and a client device identifier to an authentication function, which compares the received passcode to plural passcodes generated based on information and iterations of a value stored in correspondence with the client device identifier. Authentication is confirmed when a generated passcode matches the passcode from the client device.

Abstract: Embodiments described herein provide for passive caller verification and/or passive fraud risk assessments for calls to customer call centers. Systems and methods may be used in real time as a call is coming into a call center. An analytics server of an analytics service looks at the purported Caller ID of the call, as well as the unaltered carrier metadata, which the analytics server then uses to generate or retrieve one or more probability scores using one or more lookup tables and/or a machine-learning model. A probability score indicates the likelihood that information derived using the Caller ID information has occurred or should occur given the carrier metadata received with the inbound call. The one or more probability scores be used to generate a risk score for the current call that indicates the probability of the call being valid (e.g., originated from a verified caller or calling device, non-fraudulent).

Abstract: Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner.

Abstract: The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person.

Abstract: According to an embodiment of the disclosure, a toll-free telecommunications validation system determines a confidence value that an incoming phone call to an enterprises' toll-free number is originating from the station it purports to be by incorporating one or more layers of signals and data in determining said confidence value. The data and signals can include one or more call identifiers and/or toll-free call routing logs, service control point (SCP) signals and data, service data point (SDP) signals and data, dialed number information service (DNIS) signals and data, session initiation protocol (SIP) signals and data, carrier identification code (CIC) signals and data, location routing number (LRN) signals and data, jurisdiction information parameter (JIP) signals and data, charge number (CN) signals and data, billing number (BN) signals and data, and originating carrier information (such as information derived from the ANI).

Abstract: The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person.

Abstract: Embodiments described herein provide for automatically authenticating telephone calls to an enterprise call center. The system disclosed herein builds on the trust of a data channel for the telephony channel. Certain types of authentication information can be received through the telephony channel, as well. But the mobile application associated with the call center system may provide additional or alternative forms of data through the data channel. The system may send requests to a mobile application of a device to provide information that can reliably be assumed to be coming from that particular device, such as a state of the device and/or a user's response to push notifications. In some cases, the authentication processes may be based on quantity and quality of matches between certain metadata or attributes expected to be received from a given device as compared to the metadata or attributes received.

Abstract: Disclosed are systems and methods including software processes executed by a server for obtaining, by a computer, an audio signal including synthetic speech, extracting, by the computer, metadata from a watermark of the audio signal by applying a set of keys associated with a plurality of text-to-speech (TTS) services to the audio signal, the metadata indicating an origin of the synthetic speech in the audio signal, and generating, by the computer, based on the extracted metadata, a notification indicating that the audio signal includes the synthetic speech.

Abstract: Embodiments described herein provide for a machine-learning architecture for modeling quality measures for enrollment signals. Modeling these enrollment signals enables the machine-learning architecture to identify deviations from expected or ideal enrollment signal in future test phase calls. These differences can be used to generate quality measures for the various audio descriptors or characteristics of audio signals. The quality measures can then be fused at the score-level with the speaker recognition's embedding comparisons for verifying the speaker. Fusing the quality measures with the similarity scoring essentially calibrates the speaker recognition's outputs based on the realities of what is actually expected for the enrolled caller and what was actually observed for the current inbound caller.

Abstract: Utterances of at least two speakers in a speech signal may be distinguished and the associated speaker identified by use of diarization together with automatic speech recognition of identifying words and phrases commonly in the speech signal. The diarization process clusters turns of the conversation while recognized special form phrases and entity names identify the speakers. A trained probabilistic model deduces which entity name(s) correspond to the clusters.

Abstract: Embodiments described herein provide for performing a risk assessment. A computer identifies and stores heterogeneous events between a user and a provider system in which the user interacts with an account. The computer may store the heterogeneous events in a table. The stored event information normalizes the events associated with an account. The computer may determine static risk contributions associated with the event information of the account and store the static risk contributions in the table. The computer groups the static risk contributions into predetermined groups. The static risk contributions in each group are converted into dynamic risk contributions. The dynamic risk contributions of each group are aggregated, and the aggregate value of the dynamic risk contributions are fed to a machine learning model. The machine learning model determines a risk score associated with the account.

Abstract: Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and/or continuously, in some cases in addition to actively and/or statically, enrolling speakers. The system may dynamically generate and update profiles corresponding to end-users who contact a call center. The system may determine a level of enrollment for the enrollee profiles that limits the types of functions that the user may access. The system may update the profiles as new contact events are received or based on certain temporal triggering conditions.

Abstract: The embodiments execute machine-learning architectures for biometric-based identity recognition (e.g., speaker recognition, facial recognition) and deepfake detection (e.g., speaker deepfake detection, facial deepfake detection). The machine-learning architecture includes layers defining multiple scoring components, including sub-architectures for speaker deepfake detection, speaker recognition, facial deepfake detection, facial recognition, and lip-sync estimation engine. The machine-learning architecture extracts and analyzes various types of low-level features from both audio data and visual data, combines the various scores, and uses the scores to determine the likelihood that the audiovisual data contains deepfake content and the likelihood that a claimed identity of a person in the video matches to the identity of an expected or enrolled person.

Abstract: Aspects of the invention determining a threat score of a call traversing a telecommunications network by leveraging the signaling used to originate, propagate and terminate the call. Outer-edge data utilized to originate the call may be analyzed against historical, or third party real-time data to determine the propensity of calls originating from those facilities to be categorized as a threat. Storing the outer edge data before the call is sent over the communications network permits such data to be preserved and not subjected to manipulations during traversal of the communications network. This allows identification of threat attempts based on the outer edge data from origination facilities, thereby allowing isolation of a compromised network facility that may or may not be known to be compromised by its respective network owner.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice “liveness” detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

Abstract: Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”) in a call conversation. Embodiments include systems and methods for detecting fraudulent presentation attacks using multiple functional engines that implement various fraud-detection techniques, to produce calibrated scores and/or fused scores. A computer may, for example, evaluate the audio quality of speech signals within audio signals, where speech signals contain the speech portions having speaker utterances.