UTILIZING MACHINE LEARNING MODELS TO PROVIDE COGNITIVE SPEAKER FRACTIONALIZATION WITH EMPATHY RECOGNITION

A device may receive audio data identifying a plurality of speakers and may process the audio data, with a plurality of clustering models, to identify a plurality of speaker segments. The device may determine a plurality of diarization error rates for the plurality of speaker segments and may identify a plurality of errors in the plurality of speaker segments. The device may select rectification models to rectify the plurality of errors and may segment and/or re-segment the audio data with the rectification models to generate re-segmented audio data. The device may determine a plurality of modified diarization error rates for the plurality of speaker segments based on the re-segmented audio data and may select one of the plurality of speaker segments based on the plurality of modified diarization error rates. The device may calculate an empathy score based on the selected speaker segment and may perform actions based on the empathy score.