MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition
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abstract
Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7\% relative improvement in WER is reported at -3 SNR dB
verdicts
UNVERDICTED 2representative citing papers
3D-2D-CNN-BLSTM with word-CTC reaches 1.3% WER on GRID seen-speaker lipreading (55% relative gain over LCANet) and 8.6% on unseen speakers (24.5% gain over LipNet).
citing papers explorer
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A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.
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LipReading with 3D-2D-CNN BLSTM-HMM and word-CTC models
3D-2D-CNN-BLSTM with word-CTC reaches 1.3% WER on GRID seen-speaker lipreading (55% relative gain over LCANet) and 8.6% on unseen speakers (24.5% gain over LipNet).