SoundHound AI IP, LLC. (20250014582). METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA: Difference between revisions
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==Inventor(s)== | ==Inventor(s)== | ||
[[:Category:Kiersten L. Bradley of Santa Clara CA | [[:Category:Kiersten L. Bradley of Santa Clara CA US|Kiersten L. Bradley of Santa Clara CA US]][[Category:Kiersten L. Bradley of Santa Clara CA US]] | ||
[[:Category:Ethan Coeytaux of Boulder CO | [[:Category:Ethan Coeytaux of Boulder CO US|Ethan Coeytaux of Boulder CO US]][[Category:Ethan Coeytaux of Boulder CO US]] | ||
[[:Category:Ziming Yin of Toronto | [[:Category:Ziming Yin of Toronto CA|Ziming Yin of Toronto CA]][[Category:Ziming Yin of Toronto CA]] | ||
==METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA== | ==METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA== | ||
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This abstract first appeared for US patent application 20250014582 titled 'METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA | This abstract first appeared for US patent application 20250014582 titled 'METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA | ||
==Original Abstract Submitted== | ==Original Abstract Submitted== | ||
Latest revision as of 08:45, 25 March 2025
METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA
Organization Name
Inventor(s)
Kiersten L. Bradley of Santa Clara CA US
Ethan Coeytaux of Boulder CO US
METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA
This abstract first appeared for US patent application 20250014582 titled 'METHOD AND SYSTEM FOR CONVERSATION TRANSCRIPTION WITH METADATA
Original Abstract Submitted
methods and systems for enabling an efficient review of meeting content via a metadata-enriched, speaker-attributed and multiuser-editable transcript are disclosed. by incorporating speaker diarization and other metadata, the system can provide a structured and effective way to review and/or edit the transcript by one or more editors. one type of metadata can be image or video data to represent the meeting content. furthermore, the present subject matter utilizes a multimodal diarization model to identify and label different speakers. the system can synchronize various sources of data, e.g., audio channel data, voice feature vectors, acoustic beamforming, image identification, and extrinsic data, to implement speaker diarization.
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