Google Open-Sources Speaker Diarization AI Technology, Claims 92% Accuracy
Google has developed a research paper called Fully Supervised Speaker Diarization where they introduced a new model that uses supervised speaker labels in a more effective manner over traditional approaches. Within this model, an estimation takes place which identifies the number of speakers that participate in a conversation, which increases the amount of labeled data.
As part of NIST SRE 2000 CALLHOME benchmarking, Google’s techniques achieved a diarization error rate (DER) as low as 7.6% where DER is defined as a “percentage of the input signal that is wrongly labeled by the diarization output.” The recent results are improvements over the 8.8% DER achieved using a clustering-based method or the 9.9% DER achieved using deep neural network embedding methods.
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