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Found 4 papers, 0 papers with code
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and why is it effective in those cases?
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
However, under an unsupervised-style setting, typical training algorithms for controllable sequence generative models suffer from the training-inference mismatch, where the same sample is used as content and style input during training but unpaired samples are given during inference.
Learning Human Activities and Object Affordances from RGB-D Videos
Given a RGB-D video, we jointly model the human activities and object affordances as a Markov random field where the nodes represent objects and sub-activities, and the edges represent the relationships between object affordances, their relations with sub-activities, and their evolution over time.
Ranked #3 on
Skeleton Based Action Recognition
on CAD-120
