Jiby Mariya Jose

An efficient sarcasm detection in audio using parameter-reduced depthwise cnn

• Authors Details :  
• Jiby Mariya Jose,  
• Jeeva Jose

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In this study, we implement a lightweight CNN for sarcasm detection using audio input. To achieve this goal, we propose DepthFire block. We propose a lightweight version of the traditional Depthwise convolution layer that focuses on reduced memory. Unlike the traditional depthwise convolution layer that focuses on reducing the memory requirements of the entire architecture, our solution offers a specific and targeted approach that specifically reduces the memory requirements of the depthwise convolution layer through parameter reduction.We evaluated the impact of its energy consumption and the performance of our proposed solution with other existing solutions and on different activations, pooling functions and datasets.We further tested the applicability of the solution on 2D input.And our solution obtained 82.98 percent model size reduction as compared to MobileNetV2 and 58.94 percent as compared to MobileNetV3 small with a energy reduction of 56.48 percent on CIFAR10 dataset.

Article Subject Details

• Audiology and speech language pathology
• Computer Science
• Computer Science Applications
• Green chemistry
• Head Neck and Brain

Article Keywords Details

• Lightweight CNN Design
• Brain-Tumour Detection
• Deepfake-Detection
• Energy-Efficiency
• Sarcastic SpeechEmotion Recognition
• Energy-Efficient CNN Design
• Reduce Training Time

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