Speaker diarization improvements: new languages, increased accuracy

AssemblyAI has recently updated its Speaker diarization service, improving accuracy by up to 13% and adding support for five additional languages. The new Speaker Diarization model demonstrates a 10.1% improvement on Diarization Error Rate (DER) and an 85.4% reduction in speaker count errors. These improvements stem from recent upgrades, including the new Speech Recognition model Universal-1, an improved embedding model, and increased input sampling frequency. The enhanced service is now available for testing via a no-code Playground or by using AssemblyAI's Python SDK with an API key.

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