AI RESEARCH
Exploring Token-Space Manipulation in Latent Audio Tokenizers
arXiv CS.AI
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ArXi:2605.11192v1 Announce Type: cross Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as frame-level sequences, making it difficult to study or intervene on global factors of variation. In this work, we propose the Latent Audio Tokenizer for Token-space Editing (LATTE) that appends a fixed set of learnable latent tokens to the audio feature sequence and retains only these tokens for quantization and decoding.