Speaker Conditioning

Speaker conditioning in speech processing aims to improve models' ability to handle speaker variability, enhancing performance across diverse voices and conditions. Current research focuses on incorporating speaker information into various architectures, including Conformers and flow-based models, using techniques like Bayesian learning, speaker-normalized affine coupling layers, and disentangled networks to achieve efficient and robust speaker adaptation. These advancements are crucial for improving the accuracy and robustness of speech recognition, anti-spoofing, text-to-speech, and speaker separation systems, leading to more natural and personalized user experiences in various applications.