Verint Systems, Inc.

Verint Americas Inc.

Ido Shapira

Omer Ziv

Ran Achituv

Jeremie Dreyfuss

Ron Wein

Oana Sidi

Daniel Mark Baum

Alex Gorodetski

Roni Romano

Michael Brand

Galia Zacay

Ofer Shochet

Sagi Nachum

Yaron Gvili

Yitzhak Stauber

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.

Diarization using acoustic labeling

System and method of video capture and search optimization for creating an acoustic voiceprint

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.

Word-level blind diarization of recorded calls with arbitrary number of speakers

Methods, systems, and computer readable media for automated transcription model adaptation includes obtaining audio data from a plurality of audio files. The audio data is transcribed to produce at least one audio file transcription which represents a plurality of transcription alternatives for each audio file. Speech analytics are applied to each audio file transcription. A best transcription is selected from the plurality of transcription alternatives for each audio file. Statistics from the selected best transcription are calculated. An adapted model is created from the calculated statistics.

System and method of automated model adaptation

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.

System and method of diarization and labeling of audio data

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.

Diarization using linguistic labeling

Diarization using linguistic labeling with segmented and clustered diarized textual transcripts

Diarization using acoustic labeling to create an acoustic voiceprint

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Tel Aviv, Israel

Ido Shapira