  This report describes the submission of HYU ASML team to the IEEE Signal
Processing Cup 2024 (SP Cup 2024). This challenge, titled "ROBOVOX: Far-Field
Speaker Recognition by a Mobile Robot," focuses on speaker recognition using a
mobile robot in noisy and reverberant conditions. Our solution combines the
result of deep residual neural networks and time-delay neural network-based
speaker embedding models. These models were trained on a diverse dataset that
includes French speech. To account for the challenging evaluation environment
characterized by high noise, reverberation, and short speech conditions, we
focused on data augmentation and training speech duration for the speaker
embedding model. Our submission achieved second place on the SP Cup 2024 public
leaderboard, with a detection cost function of 0.5245 and an equal error rate
of 6.46%.
