Fine-tuning Wav2Vec 2.0 on a custom Kurdish corpus is reported to cut speaker diarization error by 7.2 percentage points and raise cluster purity by about 13 percentage points, though the paper contains conflicting numbers.
KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis,
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Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning
Fine-tuning Wav2Vec 2.0 on a custom Kurdish corpus is reported to cut speaker diarization error by 7.2 percentage points and raise cluster purity by about 13 percentage points, though the paper contains conflicting numbers.