6 citations · 9 across the 13 of their papers we have counts for
Showing 2024 · eess.ASShow all
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eess.AS2024★ 1 cited
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation
Wenyi Yu, Siyin Wang, Xiaoyu Yang +7
Full-duplex multimodal large language models (LLMs) provide a unified framework for addressing diverse speech understanding and generation tasks, enabling more natural and seamless…
eess.AS2024
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
Minglun Han, Ye Bai, Chen Shen +6
Speech self-supervised pre-training can effectively improve the performance of downstream tasks. However, previous self-supervised learning (SSL) methods for speech, such as HuBERT…
eess.AS2024★ 6 cited
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Ye Bai, Jingping Chen, Jitong Chen +52
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific con…