5 papers
RAF: Relativistic Adversarial Feedback For Universal Speech Synthesis
Yongjoon Lee, Jung-Woo Choi
We propose Relativistic Adversarial Feedback (RAF), a novel training objective for GAN vocoders that improves in-domain fidelity and generalization to unseen scenarios. Although mo…
SEMamba++: A General Speech Restoration Framework Leveraging Global, Local, and Periodic Spectral Patterns
Yongjoon Lee, Jung-Woo Choi
General speech restoration demands techniques that can interpret complex speech structures under various distortions. While State-Space Models like SEMamba have advanced the state-…
MATE: Matryoshka Audio-Text Embeddings for Open-Vocabulary Keyword Spotting
Youngmoon Jung, Myunghun Jung, Joon-Young Yang +3
Open-vocabulary keyword spotting (KWS) with text-based enrollment has emerged as a flexible alternative to fixed-phrase triggers. Prior utterance-level matching methods, from an em…
Adversarial Deep Metric Learning for Cross-Modal Audio-Text Alignment in Open-Vocabulary Keyword Spotting
Youngmoon Jung, Yong-Hyeok Lee, Myunghun Jung +3
For text enrollment-based open-vocabulary keyword spotting (KWS), acoustic and text embeddings are typically compared at either the phoneme or utterance level. To facilitate this,…
Text-Aware Adapter for Few-Shot Keyword Spotting
Youngmoon Jung, Jinyoung Lee, Seungjin Lee +3
Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room fo…