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Models/pyannote (MFCC)

pyannote (MFCC)

Reported on 9 benchmarks across 1 task · 1 paper · 3 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Speech9 results

Speaker DiarizationonDIHARD
FA· 2019-11-04
6.8
SOTA
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonAMI
DER(%)· 2019-11-04
6.3
SOTA
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonAMI
Miss· 2019-11-04
2.7
SOTA
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonETAPE
DER(%)· 2019-11-04
5.6
best: 7.7 (Baseline)
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonETAPE
FA· 2019-11-04
5.2
best: 7.5 (Baseline)
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonETAPE
Miss· 2019-11-04
0.4
best: 0.7 (pyannote (waveform))
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonDIHARD
DER(%)· 2019-11-04
10.5
best: 11.2 (Baseline (the best result in the literature as of Oct.2019))
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonDIHARD
Miss· 2019-11-04
3.7
best: 4.7 (Baseline (the best result in the literature as of Oct.2019))
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255
Speaker DiarizationonAMI
FA· 2019-11-04
3.5
best: 3.6 (pyannote (waveform))
pyannote.audio: neural building blocks for speaker diarization arXiv:1911.01255