The Visual and Audio Performance Analysis for Homogeneous Modality Transfer Learning Using Opinion Mining

Homogeneous modality transfer learning is the process of training models to recognize the same objects or events based on different modalities. This type of learning has potential applications in a variety of areas such as automated speech recognition, object recognition and gesture recognition. This paper investigates the use of visual and audio performance analysis for homogeneous modality transfer learning. Specifically, we propose a visual and audio feature-based transfer learning approach that can improve the performance of transfer learning tasks on different datasets. To evaluate this approach, we conduct experiments on two public datasets: Google Speech Commands and Image Net. The results demonstrate the effectiveness of the proposed approach in improving the performance of transfer learning tasks. In comparison with state-of-the-art approaches, the proposed approach outperforms them by up to 15% in terms of accuracy. Additionally, this approach is also resistant to the effects of data augmentation and ignores instances with noise. This makes the proposed approach suitable for heterogeneous applications.