From the 1 of 10 linked papers with an AI index.
10 papers
Fréchet Distance Loss on Speech Representations for Text-to-Speech Synthesis
Ho-Lam Chung, Kuan-Po Huang, Bo-Ru Lu +1
The paper introduces a Speech Representation Fréchet Distance loss (SR‑FD) that regularizes few‑step diffusion/flow‑matching TTS models by matching the statistics of Whisper and CT…
FdAudio: MeanFlow-Anchored Fréchet-Distance Post-Training for One-Step Text-to-Audio Generation
Kuan-Po Huang, Bo-Ru Lu, Ho-Lam Chung +2
While recent few-step sampling text-to-audio generation models like MeanAudio substantially accelerate generation by modeling average velocities, their strict one-step generation q…
Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim +8
Autoregressive (AR) models with diffusion heads have recently achieved strong text-to-audio performance, yet their iterative decoding and multi-step sampling process introduce high…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction
Shu-wen Yang, Byeonggeun Kim, Kuan-Po Huang +8
Autoregressive next-token prediction with the Transformer decoder has become a de facto standard in large language models (LLMs), achieving remarkable success in Natural Language P…
A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data
Cheng-Kang Chou, Chan-Jan Hsu, Ho-Lam Chung +5
We propose a self-refining framework that enhances ASR performance with only unlabeled datasets. The process starts with an existing ASR model generating pseudo-labels on unannotat…