3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition
This addresses audio-visual recognition tasks for applications like speech recognition with corrupted audio or speaker verification in multi-speaker scenarios, representing a domain-specific incremental improvement.
The paper tackles the problem of finding correspondence between audio and visual streams for cross-modal matching by proposing a coupled 3D-CNN architecture that jointly incorporates spatial and temporal information. The method achieves relative improvements of over 20% on Equal Error Rate and over 7% on Average Precision compared to state-of-the-art approaches.
Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this work. We propose the use of a coupled 3D Convolutional Neural Network (3D-CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features. The proposed architecture will incorporate both spatial and temporal information jointly to effectively find the correlation between temporal information for different modalities. By using a relatively small network architecture and much smaller dataset for training, our proposed method surpasses the performance of the existing similar methods for audio-visual matching which use 3D CNNs for feature representation. We also demonstrate that an effective pair selection method can significantly increase the performance. The proposed method achieves relative improvements over 20% on the Equal Error Rate (EER) and over 7% on the Average Precision (AP) in comparison to the state-of-the-art method.