End-to-End Speaker Diarization for an Unknown Number of Speakerswith Encoder-Decoder Based Attractors
Table 5: DERs (%) on simulated mixtures of a flexible number of speakers.
['[EMPTY]', 'Sim1spk', 'Sim2spk', 'Sim3spk', 'Sim4spk']
We also evaluated our method on a condition involving a flexible number of speakers. In this case, the order of the embeddings was always shuffled. The model was first finetuned from the weights trained on Sim2spk and evaluated on simulated mixtures of a flexible number of speakers. Our method achieved better DERs than the x-vector clustering-based method. It achieved 4.33\char37 and 8.94\char37 DERs on two- and three-speaker mixtures, which showed only 1.64 and 0.56 point degradation from two- or three-speaker specific models, respectively. Furthermore, our method further improved performance when the actual number of speakers was given, while x-vector clustering worsened performance in most cases using the oracle number of speakers.