1 citations · 1 across the 3 of their papers we have counts for
4 papers
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…
ICMC-ASR: The ICASSP 2024 In-Car Multi-Channel Automatic Speech Recognition Challenge
He Wang, Pengcheng Guo, Yue Li +13
To promote speech processing and recognition research in driving scenarios, we build on the success of the Intelligent Cockpit Speech Recognition Challenge (ICSRC) held at ISCSLP 2…
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
Jing Pan, Jian Wu, Yashesh Gaur +4
We present a cost-effective method to integrate speech into a large language model (LLM), resulting in a Contextual Speech Model with Instruction-following/in-context-learning Capa…
t-SOT FNT: Streaming Multi-talker ASR with Text-only Domain Adaptation Capability
Jian Wu, Naoyuki Kanda, Takuya Yoshioka +3
Token-level serialized output training (t-SOT) was recently proposed to address the challenge of streaming multi-talker automatic speech recognition (ASR). T-SOT effectively handle…