Search Results for author:
Found 8 papers, 1 papers with code
Audio-visual fine-tuning of audio-only ASR models
Audio-visual automatic speech recognition (AV-ASR) models are very effective at reducing word error rates on noisy speech, but require large amounts of transcribed AV training data.
On Robustness to Missing Video for Audiovisual Speech Recognition
Multi-modal models need to be robust: missing video frames should not degrade the performance of an audiovisual model to be worse than that of a single-modality audio-only model.
A Closer Look at Audio-Visual Multi-Person Speech Recognition and Active Speaker Selection
As an alternative, recent work has proposed to address the two problems simultaneously with an attention mechanism, baking the speaker selection problem directly into a fully differentiable model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
End-to-End Multi-Person Audio/Visual Automatic Speech Recognition
Traditionally, audio-visual automatic speech recognition has been studied under the assumption that the speaking face on the visual signal is the face matching the audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Best of Both Worlds: Multi-task Audio-Visual Automatic Speech Recognition and Active Speaker Detection
Under noisy conditions, automatic speech recognition (ASR) can greatly benefit from the addition of visual signals coming from a video of the speaker's face.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video
We achieve the state of the art performance of the audio-visual recognition on the LRS3-TED after fine-tuning our model (1. 6% WER).
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Audio-Visual Speech Recognition is Worth 32$\times$32$\times$8 Voxels
In this work, we propose to replace the 3D convolutional visual front-end with a video transformer front-end.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+5
Recurrent Neural Network Transducer for Audio-Visual Speech Recognition
This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture.
Ranked #5 on
Audio-Visual Speech Recognition
on LRS3-TED
(using extra training data)
