Exploring Token-Space Manipulation in Latent Audio Tokenizers
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. This design produces a compact, non-temporally aligned bottleneck in which each token can aggregate global information across the full utterance. We show that the resulting tokenizer preserves competitive reconstruction quality in l...
| Authors | Francesco Paissan, Luca Della Libera, Mirco Ravanelli et al. |
|---|---|
| Primary category | cs.SD |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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