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20222025
most citedIncremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff

4 citations · 8 across the 9 of their papers we have counts for

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cs.CL2025

CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset

Brian Yan, Injy Hamed, Shuichiro Shimizu +24

We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4…

cs.CL2023

Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data

Yifan Peng, Jinchuan Tian, Brian Yan +13

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech dat…

cs.CL2023

Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning

William Chen, Jiatong Shi, Brian Yan +6

Multilingual self-supervised learning (SSL) has often lagged behind state-of-the-art (SOTA) methods due to the expenses and complexity required to handle many languages. This furth…

cs.CL2023

Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing

Brian Yan, Xuankai Chang, Antonios Anastasopoulos +2

Recent works in end-to-end speech-to-text translation (ST) have proposed multi-tasking methods with soft parameter sharing which leverage machine translation (MT) data via secondar…

cs.CL20234 cited

Incremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff

Peter Polák, Brian Yan, Shinji Watanabe +2

Blockwise self-attentional encoder models have recently emerged as one promising end-to-end approach to simultaneous speech translation. These models employ a blockwise beam search…

cs.CL2023

Bayes Risk Transducer: Transducer with Controllable Alignment Prediction

Jinchuan Tian, Jianwei Yu, Hangting Chen +4

Automatic speech recognition (ASR) based on transducers is widely used. In training, a transducer maximizes the summed posteriors of all paths. The path with the highest posterior…