19 citations · 40 across the 21 of their papers we have counts for
7 papers · 1 filter
Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks
Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77
Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…
SyllableLM: Learning Coarse Semantic Units for Speech Language Models
Alan Baade, Puyuan Peng, David Harwath
Language models require tokenized inputs. However, tokenization strategies for continuous data like audio and vision are often based on simple heuristics such as fixed sized convol…
Action2Sound: Ambient-Aware Generation of Action Sounds from Egocentric Videos
Changan Chen, Puyuan Peng, Ami Baid +4
Generating realistic audio for human actions is important for many applications, such as creating sound effects for films or virtual reality games. Existing approaches implicitly a…
VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild
Puyuan Peng, Po-Yao Huang, Shang-Wen Li +2
We introduce VoiceCraft, a token infilling neural codec language model, that achieves state-of-the-art performance on both speech editing and zero-shot text-to-speech (TTS) on audi…
SpeechCLIP+: Self-supervised multi-task representation learning for speech via CLIP and speech-image data
Hsuan-Fu Wang, Yi-Jen Shih, Heng-Jui Chang +5
The recently proposed visually grounded speech model SpeechCLIP is an innovative framework that bridges speech and text through images via CLIP without relying on text transcriptio…
Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
Hung-Chieh Fang, Nai-Xuan Ye, Yi-Jen Shih +5
Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks. However, these models predominantly centered on frame-level training o…