Abstract:
Systems, methods, and media for the application of funnel analysis using desktop analytics and textual analytics to map and analyze the flow of customer service interactions. In an example implementation, the method includes: defining at least one flow that is representative of a series of events comprising at least one speech event, at least one Data Processing Activity (DPA) event, and at least one Computer Telephone Integration (CTI) event; receiving customer service interaction data comprising communication data, DPA metadata, and CTI metadata; applying the at least one flow to the customer service interaction data; determining if the customer service interaction data meets the at least one flow; and producing an automated indication based upon the determination.
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:
The system and method of separating speakers in an audio file including obtaining an audio file. The audio file is transcribed into at least one text file by a transcription server. Homogenous speech segments are identified within the at least one text file. The audio file is segmented into homogenous audio segments that correspond to the identified homogenous speech segments. The homogenous audio segments of the audio file are separated into a first speaker audio file and second speaker audio file the first speaker audio file and the second speaker audio file are transcribed to produce a diarized transcript.
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 methods of automated adaptation of a language model for transcription of audio data include obtaining audio data. The audio data is transcribed with a language model to produce a plurality of audio file transcriptions. A quality of the plurality of audio file transcriptions is evaluated. At least one best transcription from a plurality of audio file transcriptions is selected based upon the evaluated quality. Statistics are calculated from the selected at least one best transcription from the plurality of audio file transcriptions. The language model is modified from the calculated statistics.
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:
Systems and methods of diarization using linguistic labeling include receiving a set of diarized textual transcripts. At 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 transcribed customer service interaction.
Abstract:
A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.
Abstract:
A traffic-monitoring system that monitors encrypted traffic exchanged between IP addresses used by devices and a network, and further receives the user-action details that are passed over the network. By correlating between the times at which the encrypted traffic is exchanged and the times at which the user-action details are received, the system associates the user-action details with the IP addresses. In particular, for each action specified in the user-action details, the system identifies one or more IP addresses that may be the source of the action. Based on the IP addresses, the system may identify one or more users who may have performed the action. The system may correlate between the respective action-times of the encrypted actions and the respective approximate action-times of the indicated actions. The system may hypothesize that the indicated action may correspond to one of the encrypted actions having these action-times.
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.