Unseen Speaker

Unseen speaker challenges in speech processing focus on improving the robustness of systems to handle speakers, words, or acoustic conditions not encountered during training. Current research emphasizes developing denoising frameworks and meta-learning approaches, often incorporating techniques like self-conditioned CTC, relation networks, and learning-based interpolation, to enhance performance in noisy environments and with limited training data for new speakers. These advancements are crucial for improving the reliability and generalizability of speech recognition, speaker verification, and voice cloning systems in real-world applications where variability is inherent. The ultimate goal is to create more robust and adaptable speech technologies that perform consistently across diverse and unpredictable conditions.