Jing Peng
6 indexed papers
Publications per year
Top categories
Frequent co-authors
Research Timeline
LoSATok proposes a low-dimensional semantic-acoustic tokenizer that efficiently compresses high-dimensional audio features into a compact latent space, significantly improving the performance and efficiency of audio generation models.
LoSATok proposes a low-dimensional semantic-acoustic tokenizer that efficiently compresses high-dimensional audio features into a compact latent space, significantly improving the performance and efficiency of audio generation models.
The paper introduces SURE, a unified framework designed to standardize and improve the comparability and reproducibility of evaluations for advanced speech understanding models.
The paper introduces SURE, a unified framework designed to standardize and improve the comparability and reproducibility of evaluations for advanced speech understanding models.
This paper introduces UniAE-MoE, a unified audio encoder that achieves state-of-the-art performance in audio understanding tasks.
This paper proposes VISH, a training-free framework for selecting a compact hotword subset from large dynamic inventories.
Papers
VISH-ASR: Viterbi-Guided Selection of Hotwords for Speech-LLM Contextual ASR
Xiaoyu Gu, Yu Xi, Chencheng Wang, Zonghan Zhou +3 more
This paper proposes VISH, a training-free framework for selecting a compact hotword subset from large dynamic inventories.