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Found 8 papers, 0 papers with code
Audiovisual Highlight Detection in Videos
To better study the task of highlight detection, we run a pilot experiment with highlights annotations for a small subset of video clips and fine-tune our best model on it.
Self-Supervised learning with cross-modal transformers for emotion recognition
Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language.
Multi-modal embeddings using multi-task learning for emotion recognition
General embeddings like word2vec, GloVe and ELMo have shown a lot of success in natural language tasks.
Multimodal and Multiresolution Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Multiresolution and Multimodal Speech Recognition with Transformers
We particularly focus on the scene context provided by the visual information, to ground the ASR.
Robust Multi-channel Speech Recognition using Frequency Aligned Network
In this paper, we further develop this idea and use frequency aligned network for robust multi-channel automatic speech recognition (ASR).
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning
Using a large offline teacher model trained on beamformed audio, we trained a simpler multi-channel student acoustic model used in the speech recognition system.
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
For real-world speech recognition applications, noise robustness is still a challenge.
