Discriminative Multi-modality Speech Recognition

CVPR 2020  ·  Bo Xu, Cheng Lu, Yandong Guo, Jacob Wang ·

Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates. After combining visual modality, ASR is upgraded to the multi-modality speech recognition (MSR). In this paper, we propose a two-stage speech recognition model. In the first stage, the target voice is separated from background noises with help from the corresponding visual information of lip movements, making the model 'listen' clearly. At the second stage, the audio modality combines visual modality again to better understand the speech by a MSR sub-network, further improving the recognition rate. There are some other key contributions: we introduce a pseudo-3D residual convolution (P3D)-based visual front-end to extract more discriminative features; we upgrade the temporal convolution block from 1D ResNet with the temporal convolutional network (TCN), which is more suitable for the temporal tasks; the MSR sub-network is built on the top of Element-wise-Attention Gated Recurrent Unit (EleAtt-GRU), which is more effective than Transformer in long sequences. We conducted extensive experiments on the LRS3-TED and the LRW datasets. Our two-stage model (audio enhanced multi-modality speech recognition, AE-MSR) consistently achieves the state-of-the-art performance by a significant margin, which demonstrates the necessity and effectiveness of AE-MSR.

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Results from the Paper

Ranked #6 on Audio-Visual Speech Recognition on LRS3-TED (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Lipreading Lip Reading in the Wild 3D Conv + P3D-ResNet50 + TCN Top-1 Accuracy 84.80 # 12
Audio-Visual Speech Recognition LRS3-TED EG-seq2seq Word Error Rate (WER) 6.8 # 6
Lipreading LRS3-TED EG-seq2seq Word Error Rate (WER) 57.8 # 11