Generative Adversarial Networks Embedding Refinement for Speaker Diarization Improvement - Pande, Vinod; Kale, Dr. Vijay K.; Kayte, Dr. Sangramsing N. (INFOCOMP Journal of Computer Science, 2025)
The purpose of this research is to incorporate Generative Adversarial Networks(GAN) into the speaker diarization process by refining embeddings along with the overlapping speech and noise problems. In this case, better speaker embeddings are produced by GANs through adversarial learning, which makes them more separable and more powerful than traditional embedding techniques. The practical assessment of the system used the AMI Meeting Corpus as well as the VoxConverse data sets and performance was evaluated across different acoustic conditions. The results support very substantial performance advantages with improvements of 25% in the Error Rate of Dialysis (DER) in comparison to baseline models. Such models included x vector-based clustering and end-to-end neural diarization systems. In support of this, T-SNE again stunningly verified that the cluster separability of embeddings refined by a GAN improved. Furthermore, the system is flexible for real-world scenarios as it exhibits robust performance even under noisy overlapping speech conditions. This evidence testifies that using GAN for embedding refinement is a very effective method to address the issue of speaker diarization.