activity
20202026
most citedSelf-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modeling

19 citations · 40 across the 21 of their papers we have counts for

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Showing cs.CLShow all

5 papers · 1 filter

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2023

Audio-Visual Neural Syntax Acquisition

Cheng-I Jeff Lai, Freda Shi, Puyuan Peng +10

We study phrase structure induction from visually-grounded speech. The core idea is to first segment the speech waveform into sequences of word segments, and subsequently induce ph…

cs.CL2023

Style-transfer based Speech and Audio-visual Scene Understanding for Robot Action Sequence Acquisition from Videos

Chiori Hori, Puyuan Peng, David Harwath +7

To realize human-robot collaboration, robots need to execute actions for new tasks according to human instructions given finite prior knowledge. Human experts can share their knowl…