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Feature Spotlight: Speaker Diarization
In June 2024, we updated our Speaker Diarization model to be 13% more accurate and added support for five additional languages. These improvements help you to accurately identify who is speaking in audio recordings, making it easier to analyze conversations in more languages.
Here's how to get started:
- Identifying Speakers in Audio Recordings: This guide shows you exactly how to apply our Speaker Diarization model to distinguish between speakers in your audio projects. Check out the guide.
- Processing Speaker Labels with LeMUR: Use AssemblyAI's Speaker Diarization model to not only transcribe audio and identify speakers but also infer their names using LeMUR. Explore the guide.
How Our Speaker Diarization Model Transforms Audio Analysis:
Speaker Diarization transforms audio analysis by accurately identifying and differentiating speakers, critical for a range of applications.
- Improved Transcripts: Makes transcripts from meetings and webinars easier to navigate with added speaker labels, increasing content accessibility.
- Searchable Audio: Enables precise searches within audio files to locate specific statements or discussions, enhancing user experience on digital platforms.
- Analytics and Language Models: With accurate speaker-labelled trancripts, language-based AI tools can be trained better. For example, customer service software can use this information to better train agents and enhance how they communicate with customers.
Visit our docs to learn more.
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