1 citations · 1 across the 8 of their papers we have counts for
9 papers
D2: High-Capability Full-Duplex Spoken Language Models from Half-Duplex Backbones
Cheng-Kuang Chang, Alexander H. Liu, Brian Kingsbury +5
Full-duplex spoken language models (FD-SLMs) support simultaneous listening and speaking, but often lag behind half-duplex spoken language models (HD-SLMs) in general capabilities.…
Overcoming State Inertia in Full-Duplex Spoken Language Models via Activation Steering
Cheng-Kuang Chang, Kai-Wei Chang, Alexander H. Liu +1
Full-duplex spoken language models (FD-SLMs) enable seamless speech interaction by allowing models to listen and speak simultaneously, yet the internal mechanism by which they coor…
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…
Voxtral Realtime
Mistral-AI, :, Alexander H. Liu +166
We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adap…
USAD: Universal Speech and Audio Representation via Distillation
Heng-Jui Chang, Saurabhchand Bhati, James Glass +1
Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we pr…
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…
SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction
Shester Gueuwou, Xiaodan Du, Greg Shakhnarovich +2
Sign language processing has traditionally relied on task-specific models, limiting the potential for transfer learning across tasks. Pre-training methods for sign language have ty…
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…