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Found 4 papers, 3 papers with code
AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization
With a proper activation as an information bottleneck on content embeddings, the trade-off between the synthesis quality and the speaker similarity of the converted speech is improved drastically.
Audio and Speech Processing Sound
Input-independent Attention Weights Are Expressive Enough: A Study of Attention in Self-supervised Audio Transformers
In this paper, we seek solutions for reducing the computation complexity of transformer-based models for speech representation learning.
VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture
Voice conversion (VC) is a task that transforms the source speaker's timbre, accent, and tones in audio into another one's while preserving the linguistic content.
Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation
We use the representations with two downstream tasks, speaker identification, and phoneme classification.
