Unlike autoregressive models, discrete diffusion-based models for zero-shot text-to-speech generate speech tokens in parallel and can revisit earlier predictions. Mask-and-replace training extends mask-only training by randomly replacing some tokens, and its gains are commonly attributed to self-correction, the ability...
Hounsu Kim, Joonyong Park, Yuki Saito et al.· 0 citations
A forensic-preserving neural audio codec (FP-NAC), which fine-tunes a pretrained codec using a detector-guided objective while preserving its native hard quantization path and bitrate, and highlights forensic transparency as a codec design objective alongside perceptual quality.
Jungwoo Kim, Joonyong Park, Junyoung Koh et al.· 0 citations
This paper analyzes the token statistics of 13 NACs spanning multi-codebook residual vector quantization, single-codebook VQ, and non-VQ designs, evaluated on three corpora under clean, white-noise, and real-world DEMAND-noise conditions to provide architecture-conditioned conventions for applying language-statistical...
Joonyong Park, Shinnosuke Takamichi, David M. Chan et al.· 1 citation
The results support analyzing subjective speech rewards as predictor-axis-base tuples as predictor-axis-base tuples and provide practical diagnostics for selecting rewards before multi-reward speech post-training.
APT, a software-hardware co-designed accelerator for high-resolution DiTs that leverages attention probabilities as a unified importance metric to jointly optimize computation through fine-grained pruning and adaptive precision scaling, and is evaluated on SOTA DiT models including PixArt, Stable Diffusion 3, and FLUX.
Sungyeob Yoo, Seeyeon Kim, Joonyong Park et al.· 0 citations
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