2 citations · 3 across the 5 of their papers we have counts for
6 papers
Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition
Fengrun Zhang, Li Fu, Wangjin Zhou +3
Modern automatic speech recognition (ASR) scenarios require both spoken-form transcripts for faithful transcription and readable written-form transcripts with inverse text normaliz…
PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects
Sicheng Yang, Shulan Ruan, Shiwei Wu +4
While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of simple transcription. Existing b…
PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Li Fu, Yu Xin, Sunlu Zeng +3
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) system…
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition
Li Fu, Shanyong Yu, Siqi Li +3
Recent advancements in scaling up models have significantly improved performance in Automatic Speech Recognition (ASR) tasks. However, training large ASR models from scratch remain…
Do self-supervised speech and language models extract similar representations as human brain?
Peili Chen, Linyang He, Li Fu +3
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given the…
Neural2Speech: A Transfer Learning Framework for Neural-Driven Speech Reconstruction
Jiawei Li, Chunxu Guo, Li Fu +3
Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural…