Robust Speaker Representation

Robust speaker representation focuses on creating speech embeddings that are resilient to noise, variations in speaking style, and differences in language or recording conditions, enabling accurate speaker identification and verification across diverse scenarios. Current research emphasizes self-supervised learning methods, often employing architectures like HuBERT and variations thereof, along with techniques like disentanglement learning and data augmentation to improve model robustness. These advancements are crucial for improving the accuracy and reliability of various speech technologies, including speaker verification systems, speech recognition, and emotion recognition, particularly in challenging real-world conditions.