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Joonyong Park

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Review Oct 2026

PitchIn: Natural-Language Vocal Coaching from Unstructured Community Feedback

PitchIn, audio-language models for natural-language vocal coaching, and the PeerCoach Dataset are introduced: 14,445 instruction examples covering Focused QA, Overall Feedback, and Recognition QA and significantly preferred for usefulness.

Hayeon Bang, Hounsu Kim, Joonyong Park et al. · 0 citations
Preprint Oct 2026

Beyond Token Revision: Investigating Mask-and-Replace Diffusion for Zero-Shot Text-to-Speech

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
Preprint Sep 2026

Neural Audio Codec for Robust Audio Deepfake Detection

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
#natural language process... Preprint Aug 2026

Language-Statistical Analysis of Neural Audio Codec Tokens Across Architectures, Corpora, and Noise Conditions

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
Preprint Aug 2026

APT: Accelerating Diffusion Transformers via Attention Probability-Guided Pruning and Quantization

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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