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pyannote-segmentation-3.0-RTVE

Automatic Speech Recognition

speaker-diarization

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chsougan commited on Dec 12, 2024

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@@ -11,9 +11,11 @@ library_name: pyannote-audio
 ## Model Details
 This system is a collection of three fine-tuned models monitoring False Alarm, Missed Detection, and Speaker Confusion, to be fused with [DOVER-Lap](https://github.com/desh2608/dover-lap).
 Each model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on [the RTVE database](https://catedrartve.unizar.es/rtvedatabase.html) used for Albayzin Evaluations of IberSPEECH 2024.
 On the RTVE2024 test set it achives the following results (two-decimal rounding):
 - Diarization Error Rate (DER): 14.98%

 ## Model Details
 This system is a collection of three fine-tuned models monitoring False Alarm, Missed Detection, and Speaker Confusion, to be fused with [DOVER-Lap](https://github.com/desh2608/dover-lap).
+More information about the fusion of these models can be found in this [paper](https://www.isca-archive.org/iberspeech_2024/souganidis24_iberspeech.html)
 Each model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on [the RTVE database](https://catedrartve.unizar.es/rtvedatabase.html) used for Albayzin Evaluations of IberSPEECH 2024.
 On the RTVE2024 test set it achives the following results (two-decimal rounding):
 - Diarization Error Rate (DER): 14.98%