Speaker Variability

Speaker variability, the inherent differences in how individuals produce speech, is a central challenge in automatic speech recognition (ASR) and speaker recognition systems. Current research focuses on mitigating the impact of this variability through techniques like prototype-based adaptation, variational autoencoders for encoding speaker-specific characteristics, and attention-based mechanisms to weight the importance of different speech segments. Addressing speaker variability is crucial for improving the accuracy and robustness of ASR systems across diverse populations, including those with dysarthria, and for enhancing speaker verification technologies in real-world applications.