6 papers
Can LLMs Help Localize Fake Words in Partially Fake Speech?
Lin Zhang, Thomas Thebaud, Zexin Cai +5
Large language models (LLMs), trained on large-scale text, have recently attracted significant attention for their strong performance across many tasks. Motivated by this, we inves…
WST: Weakly Supervised Transducer for Automatic Speech Recognition
Dongji Gao, Chenda Liao, Changliang Liu +5
The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality anno…
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation
Amir Hussein, Cihan Xiao, Matthew Wiesner +3
Neural transducers (NT) provide an effective framework for speech streaming, demonstrating strong performance in automatic speech recognition (ASR). However, the application of NT…
LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization
Zengrui Jin, Yifan Yang, Mohan Shi +10
The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions…
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation
Ruizhe Huang, Mahsa Yarmohammadi, Sanjeev Khudanpur +1
Existing research suggests that automatic speech recognition (ASR) models can benefit from additional contexts (e.g., contact lists, user specified vocabulary). Rare words and name…
Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment
Yiwen Shao, Shi-Xiong Zhang, Yong Xu +4
In the field of multi-channel, multi-speaker Automatic Speech Recognition (ASR), the task of discerning and accurately transcribing a target speaker's speech within background nois…