Abstract: A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.

Abstract: Voice activity detection (VAD) is an enabling technology for a variety of speech based applications. Herein disclosed is a robust VAD algorithm that is also language independent. Rather than classifying short segments of the audio as either “speech” or “silence”, the VAD as disclosed herein employees a soft-decision mechanism. The VAD outputs a speech-presence probability, which is based on a variety of characteristics.

Abstract: A method for expanding an initial ontology via processing of communication data, wherein the initial ontology is a structural representation of language elements comprising a set of entities, a set of terms, a set of term-entity associations, a set of entity-association rules, a set of abstract relations, and a set of relation instances. A method for extracting a set of significant phrases and a set of significant phrase co-occurrences from an input set of documents further includes utilizing the terms to identify relations within the training set of communication data, wherein a relation is a pair of terms that appear in proximity to one another.

Abstract: Disclosed herein are methods of diarizing audio data using first-pass blind diarization and second-pass blind diarization that generate speaker statistical models, wherein the first pass-blind diarization is on a per-frame basis and the second pass-blind diarization is on a per-word basis, and methods of creating acoustic signatures for a common speaker based only on the statistical models of the speakers in each audio session.

Abstract: Systems and methods of script identification in audio data obtained from audio data. The audio data is segmented into a plurality of utterances. A script model representative of a script text is obtained. The plurality of utterances are decoded with the script model. A determination is made if the script text occurred in the audio data.

Abstract: Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

Abstract: By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. The ontology is built on the premise that meaningful terms are detected in the corpus and then classified according to specific semantic concepts, or entities. Once the main terms are defined, direct relations or linkages can be formed between these terms and their associated entities. Then, the relations are grouped into themes, which are groups or abstracts that contain synonymous relations. The disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The ontology programming automatically trains itself to understand the domain or environment of the communication data by processing and analyzing a defined corpus of communication data.

Abstract: Systems and methods of script identification in audio data obtained from audio data. The audio data is segmented into a plurality of utterances. A script model representative of a script text is obtained. The plurality of utterances are decoded with the script model. A determination is made if the script text occurred in the audio data.

Abstract: Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.

Abstract: A video system for video surveillance or video monitoring includes a video source, a video control system, and a video management system. The video management system is configured to receive configuration data and generate maps based on the configuration data, wherein at least one of the maps is associated with the video source. The video management system displays the map associated with the video source and displays a graphical symbol on the map which indicates the location of the video source. The video management system then receives a selection of the video source, receives information associated with the selected video source, and displays the received information.

Abstract: A video system for video surveillance or video monitoring includes a video source, a video control system, and a video management system. The video management system is configured to receive configuration data and generate maps based on the configuration data, wherein at least one of the maps is associated with the video source. The video management system displays the map associated with the video source and displays a graphical symbol on the map which indicates the location of the video source. The video management system then receives a selection of the video source, receives information associated with the selected video source, and displays the received information.

Abstract: A subject can be tracking using a plurality of physical video monitoring or image acquisition devices deployed in a delimited area. A map represents the delimited area. Icons representing the physical monitoring devices can be placed and configured on the map representing the delimited area. Some or all of the placed and configured video monitoring devices can be logically linked together to form scenes. The video feed from the physical video monitoring devices may be recorded in stable storage. A layout for display of the video feeds may be selected. Upon playing the video feed, whether live or recorded, a subject can be tracked through the delimited area by selecting one of the video monitoring devices in one of the available scenes.

Abstract: A subject can be tracking using a plurality of physical video monitoring or image acquisition devices deployed in a delimited area. A map represents the delimited area. Icons representing the physical monitoring devices can be placed and configured on the map representing the delimited area. Some or all of the placed and configured video monitoring devices can be logically linked together to form scenes. The video feed from the physical video monitoring devices may be recorded in stable storage. A layout for display of the video feeds may be selected. Upon playing the video feed, whether live or recorded, a subject can be tracked through the delimited area by selecting one of the video monitoring devices in one of the available scenes.

Abstract: A subject can be tracking using a plurality of physical video monitoring or image acquisition devices deployed in a delimited area. A map represents the delimited area. Icons representing the physical monitoring devices can be placed and configured on the map representing the delimited area. Some or all of the placed and configured video monitoring devices can be logically linked together to form scenes. The video feed from the physical video monitoring devices may be recorded in stable storage. A layout for display of the video feeds may be selected. Upon playing the video feed, whether live or recorded, a subject can be tracked through the delimited area by selecting one of the video monitoring devices in one of the available scenes.

Abstract: Systems and methods search video data for objects that satisfy a general object description. A database is populated with identified objects and object characteristics detected in video data with at least one identifier that specifies video image data. At least one search parameter is received that presents a general object description. The database is queried based upon the received at least one search parameter. At least one identifier is returned from the database based upon the at least one search parameter.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: A video system for video surveillance or video monitoring includes a video source, a video control system, and a video management system. The video management system is configured to receive configuration data and generate maps based on the configuration data, wherein at least one of the maps is associated with the video source. The video management system displays the map associated with the video source and displays a graphical symbol on the map which indicates the location of the video source. The video management system then receives a selection of the video source, receives information associated with the selected video source, and displays the received information.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.

Abstract: Embodiments for generating appropriate data sets for learning to identify user actions. A user uses one or more applications over a suitable period of time. As the user uses the applications, a monitoring device, acting as a “man-in-the-middle,” intermediates the exchange of encrypted communication between the applications and the servers that serve the applications. The monitoring device obtains, for each action performed by the user, two corresponding (bidirectional) flows of communication: an encrypted flow, and an unencrypted flow. Since the unencrypted flow indicates the type of action that was performed by the user, the correspondence between the encrypted flow and the unencrypted flow may be used to automatically label the encrypted flow, without decrypting the encrypted flow. Features of the encrypted communication may then be stored in association with the label to automatically generate appropriately-sized learning set for each application of interest.

Abstract: Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. A least one heuristic is automatedly applied to the diarized textual transcripts to select transcripts likely to be associated with an identified group of speakers. The selected transcripts are analyzed to create at least one linguistic model. The linguistic model is applied to transcripted audio data to label a portion of the transcripted audio data as having been spoken by the identified group of speakers. Still further embodiments of diarization using linguistic labeling may serve to label agent speech and customer speech in a recorded and transcripted customer service interaction.