Abstract

The performance of most speaker diarization systems with x-vector embeddings is both vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker diarization using generative adversarial network (GAN) with an encoder network (ClusterGAN) to project input x-vectors into a latent space has shown promising performance on meeting data. In this paper, we extend the ClusterGAN network to improve diarization robustness and enable rapid generalization across various challenging domains. To this end, we fetch the pre-trained encoder from the ClusterGAN and fine tune it by using prototypical loss (meta-ClusterGAN or MCGAN) under the meta-learning paradigm. Experiments are conducted on CALLHOME telephonic conversations, AMI meeting data, DIHARD-II (dev set) which includes challenging multi-domain corpus, and two child-clinician interaction corpora (ADOS, BOSCC) related …

Date

2021

Authors

Monisankha Pal, Manoj Kumar, Raghuveer Peri, Tae Jin Park, So Hyun Kim, Catherine Lord, Somer Bishop, Shrikanth Narayanan

Journal

IEEE/ACM transactions on audio, speech, and language processing

Volume

29

Pages

1204-1219

Publisher

IEEE