10 citations · 12 across the 16 of their papers we have counts for
6 papers · 1 filter
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko +11
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The an…
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning
Yifan Peng, Krishna C. Puvvada, Zhehuai Chen +7
Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models (SpeechLMs). Earlier SpeechLMs focused on…
EMMeTT: Efficient Multimodal Machine Translation Training
Piotr Żelasko, Zhehuai Chen, Mengru Wang +7
A rising interest in the modality extension of foundation language models warrants discussion on the most effective, and efficient, multimodal training approach. This work focuses…
Chain-of-Thought Prompting for Speech Translation
Ke Hu, Zhehuai Chen, Chao-Han Huck Yang +5
Large language models (LLMs) have demonstrated remarkable advancements in language understanding and generation. Building on the success of text-based LLMs, recent research has ada…
BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5
Zhehuai Chen, He Huang, Oleksii Hrinchuk +5
Incorporating speech understanding capabilities into pretrained large-language models has become a vital research direction (SpeechLLM). The previous architectures can be categoriz…