Speaker Diarization: Adding Speaker Labels for Enterprise Speech-to-Text

The transcript appears to be a conversation between two people, discussing the latest tennis news and events, as well as mentioning their involvement in the Diversity and Inclusion committee for USDA. Speaker A congratulates Speaker B on her successful performance in an exhibition match. Speaker A also talks about how Speaker Diarization opens up significant analytical opportunities for companies by identifying each speaker and enabling product teams to analyze behaviors, identify patterns and trends, and inform business strategy. The transcript also mentions some challenges and limitations of Speaker Diarization models, such as the need for speakers to talk for more than 30 seconds, background noise affecting the model's ability to accurately assign speaker labels, and overtalk or interrupting conversations making it difficult for the model to appropriately assign speaker labels. The text also provides examples of how businesses are currently leveraging Speaker Diarization to create powerful transcription and analysis tools for their customers, such as virtual meeting and hiring intelligence platforms, conversation intelligence platforms, AI subtitle generators, and call centers. Finally, the text suggests some best practices for adding Speaker Diarization to enterprise applications, including keeping in mind that Speaker Diarization models work best when each speaker speaks for at least 30 uninterrupted seconds, and there is typically a limitation of the number of speakers a Speaker Diarization model can detect.