Abstract: The present disclosure provides computing systems and methods for critical event detection and response, including event monitoring, asset intelligence, and/or mass notifications. As examples, the critical event intelligence platform described herein can be used for security, travel, logistics, finance, intelligence, and/or insurance teams responsible for business continuity, physical safety, duty of care, and/or other operational tasks. The proposed critical event intelligence platform provides users with the speed, coverage and actionability needed to respond effectively in a fast-paced and dynamic critical event environment.

Abstract: The disclosed system continuously refines a model used by an Automatic Speech Recognition (ASR) system to enable fast and accurate transcriptions of detected speech activity. The ASR system analyzes speech activity to generate text transcriptions and associated metrics (such as minimum Bayes risk and/or perplexity) that correspond to the quality of or confidence in each generated transcription. The system employs a filtering process to select certain text transcriptions based in part on one or more associated quality metrics. In addition, the system corrects for known systemic errors within the ASR system and provides a mechanism for manual review and correction of transcriptions. The system selects a subset of transcriptions based on factors including confidence score, and uses the selected subset of transcriptions to re-train the ASR model. By continuously retraining the ASR model, the system is able to provide ever faster and more accurate text transcriptions of detected speech activity.

Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a group of telephone calls having associated call characteristics and analyzes the group of telephone calls to identify and store a first set of distributions of call characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives one or more subsequent telephone calls to be analyzed. The system analyzes the received one or more telephone calls to identify a second set of distributions of call characteristics associated with the received telephone call. The system then compares the second set of distributions of call characteristics to the stored first set of distributions of call characteristics to assess a probability that the one or more received telephone calls represents normal, fraudulent, or indeterminate activity.

Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialog patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialog patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.

Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a group of telephone calls having associated call characteristics and analyzes the group of telephone calls to identify and store a first set of distributions of call characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives one or more subsequent telephone calls to be analyzed. The system analyzes the received one or more telephone calls to identify a second set of distributions of call characteristics associated with the received telephone call. The system then compares the second set of distributions of call characteristics to the stored first set of distributions of call characteristics to assess a probability that the one or more received telephone calls represents normal, fraudulent, or indeterminate activity.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: The disclosed system continuously refines a model used by an Automatic Speech Recognition (ASR) system to enable fast and accurate transcriptions of detected speech activity. The ASR system analyzes speech activity to generate text transcriptions and associated metrics (such as minimum Bayes risk and/or perplexity) that correspond to the quality of or confidence in each generated transcription. The system employs a filtering process to select certain text transcriptions based in part on one or more associated quality metrics. In addition, the system corrects for known systemic errors within the ASR system and provides a mechanism for manual review and correction of transcriptions. The system selects a subset of transcriptions based on factors including confidence score, and uses the selected subset of transcriptions to re-train the ASR model. By continuously retraining the ASR model, the system is able to provide ever faster and more accurate text transcriptions of detected speech activity.

Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a group of telephone calls having associated call characteristics and analyzes the group of telephone calls to identify and store a first set of distributions of call characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives one or more subsequent telephone calls to be analyzed. The system analyzes the received one or more telephone calls to identify a second set of distributions of call characteristics associated with the received telephone call. The system then compares the second set of distributions of call characteristics to the stored first set of distributions of call characteristics to assess a probability that the one or more received telephone calls represents normal, fraudulent, or indeterminate activity.

Abstract: A facility and method for analyzing and classifying calls without transcription via keyword spotting is disclosed. The facility uses a group of calls having known outcomes to generate one or more domain- or entity-specific grammars containing keywords and related information that are indicative of particular outcome. The facility monitors telephone calls by determining the domain or entity associated with the call, loading the appropriate grammar or grammars associated with the determined domain or entity, and tracking keywords contained in the loaded grammar or grammars that are spoken during the monitored call, along with additional information. The facility performs a statistical analysis on the tracked keywords and additional information to determine a classification for the monitored telephone call.

Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialogue patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialogue patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: A facility and method for analyzing and classifying calls without transcription. The facility analyzes individual frames of an audio to identify speech and measure the amount of time spent in speech for each channel (e.g., caller channel, agent channel). Additional telephony metrics such as R-factor or MOS score and other metadata may be factored in as audio analysis inputs. The facility then analyzes the frames together as a whole and formulates a clustered-frame representation of a conversation to further identify dialog patterns and characterize call classification. Based on the data in the clustered-frame representation, the facility is able to make estimations of call classification. The correlation of dialog patterns to call classification may be utilized to develop targeted solutions for call classification issues, target certain advertising channels over others, evaluate advertising placements at scale, score callers, and to identify spammers.

Abstract: A facility and method for analyzing and classifying calls without transcription via keyword spotting is disclosed. The facility uses a group of calls having known outcomes to generate one or more domain- or entity-specific grammars containing keywords and related information that are indicative of particular outcome. The facility monitors telephone calls by determining the domain or entity associated with the call, loading the appropriate grammar or grammars associated with the determined domain or entity, and tracking keywords contained in the loaded grammar or grammars that are spoken during the monitored call, along with additional information. The facility performs a statistical analysis on the tracked keywords and additional information to determine a classification for the monitored telephone call.

Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a first group of telephone calls having associated voice characteristics and analyzes the first group of telephone calls to identify and store a first set of distributions of voice characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives a second group of telephone calls to be analyzed. The system analyzes the second group of telephone calls to identify a second set of distributions of voice characteristics associated with the second group of telephone calls. The system then compares the second set of distributions of voice characteristics to the stored first set of distributions of voice characteristics to assess a probability that one or more telephone calls in the second group of telephone calls represents normal, fraudulent, or indeterminate activity.

Abstract: An audio/visual system (e.g., such as an entertainment console or other computing device) plays a base audio track, such as a portion of a pre-recorded song or notes from one or more instruments. Using a depth camera or other sensor, the system automatically detects that a user (or a portion of the user) enters a first collision volume of a plurality of collision volumes. Each collision volume of the plurality of collision volumes is associated with a different audio stem. In one example, an audio stem is a sound from a subset of instruments playing a song, a portion of a vocal track for a song, or notes from one or more instruments. In response to automatically detecting that the user (or a portion of the user) entered the first collision volume, the appropriate audio stem associated with the first collision volume is added to the base audio track or removed from the base audio track.

Abstract: A method for providing three-dimensional audio is provided. The method includes receiving a depth map imaging a scene from a depth camera and recognizing a human subject present in the scene. The human subject is modeled with a virtual skeleton comprising a plurality of joints defined with a three-dimensional position. A world space ear position of the human subject is determined based on the virtual skeleton. Furthermore, a target world space ear position of the human subject is determined. The target world space ear position is the world space position where a desired audio effect can be produced via an acoustic transducer array. The method further includes outputting a notification representing a spatial relationship between the world space ear position and the target world space ear position.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: A facility and method for analyzing and classifying calls without transcription via keyword spotting is disclosed. The facility uses a group of calls having known outcomes to generate one or more domain- or entity-specific grammars containing keywords and related information that are indicative of particular outcome. The facility monitors telephone calls by determining the domain or entity associated with the call, loading the appropriate grammar or grammars associated with the determined domain or entity, and tracking keywords contained in the loaded grammar or grammars that are spoken during the monitored call, along with additional information. The facility performs a statistical analysis on the tracked keywords and additional information to determine a classification for the monitored telephone call.

Abstract: A system and method for monitoring telephone calls to detect call traffic pumping activity and take corrective action is described. The system receives a group of training telephone calls having associated call audio content and associated information, and the system analyzes the training telephone calls to generate and store a classification model that correlates call features and associations with a probability of call traffic pumping activity. The system receives a subsequent monitored telephone call to be analyzed. The system analyzes the monitored telephone call to identify features present in the audio content of the monitored telephone call and other associated information. The system then compares the features and associated information to the stored classification model in order to determine a probability that the monitored telephone call is associated with call traffic pumping activity.

Abstract: A system and method for monitoring telephone calls to detect fraudulent activity and take corrective action is described. The system receives a first group of telephone calls having associated voice characteristics and analyzes the first group of telephone calls to identify and store a first set of distributions of voice characteristics that are indicative of normal activity, fraudulent activity, or indeterminate activity. The system receives a second group of telephone calls to be analyzed. The system analyzes the second group of telephone calls to identify a second set of distributions of voice characteristics associated with the second group of telephone calls. The system then compares the second set of distributions of voice characteristics to the stored first set of distributions of voice characteristics to assess a probability that one or more telephone calls in the second group of telephone calls represents normal, fraudulent, or indeterminate activity.