End-to-End Speaker Diarization for an Unknown Number of Speakerswith Encoder-Decoder Based Attractors
Table 7: DERs and JERs (%) on DIHARD eval.
['Method', 'DER', 'JER']
Finally, we evaluated our method on the DIHARD dataset. The evaluation follows the DIHARD 2019 track 2, where speech activity detection has to be conducted from single channel audio. Because utilizing a high number of speakers with PIT is difficult, our system was only trained to output the most dominant seven speakers even if the input contained more than seven speakers. Our SA-EEND with EDA achieved a DER of 32.59\char37, which outperformed the baseline [sell2018diarization] and the best pre-is2019-deadline system by the DI-IT team [novoselov2019speaker], but it could not beat the best post-is2019-deadline system by the BUT team [landini2020but]. We note that our system is based on 8kHz audio, while others use 16kHz audio with additional training data from VoxCeleb datasets [nagrani2020voxceleb]. Evaluations on high-resolution audio with additional data are left as future work.