1 citations · 1 across the 8 of their papers we have counts for
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USAD 2.0: Scaling Representation Distillation for Universal Audio Understanding
Heng-Jui Chang, Alexander H. Liu, Saurabhchand Bhati +4
Audio encoders are critical to modern audio applications as large language models (LLMs) increasingly rely on a single encoder for diverse inputs. While self-supervised learning (S…
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation
Alexander H. Liu, Sang-gil Lee, Chao-Han Huck Yang +5
Pre-training and representation learning have been playing an increasingly important role in modern speech processing. Nevertheless, different applications have been relying on dif…
A Closer Look at Neural Codec Resynthesis: Bridging the Gap between Codec and Waveform Generation
Alexander H. Liu, Qirui Wang, Yuan Gong +1
Neural Audio Codecs, initially designed as a compression technique, have gained more attention recently for speech generation. Codec models represent each audio frame as a sequence…
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective
Alexander H. Liu, Sung-Lin Yeh, James Glass
Existing studies on self-supervised speech representation learning have focused on developing new training methods and applying pre-trained models for different applications. Howev…