Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications

• URL: http://arxiv.org/abs/2410.21478v1
• Date: Mon, 28 Oct 2024 19:32:17 GMT
• Title: Knowledge Distillation for Real-Time Classification of Early Media in Voice Communications
• Authors: Kemal Altwlkany, Hadžem Hadžić, Amar Kurić, Emanuel Lacic,
• Abstract summary: We propose a novel approach for low-resource requirements based on gradient-boosted trees. We show that leveraging knowledge distillation and class aggregation techniques to train a simpler and smaller model accelerates the classification of early media in voice calls.
• Score: 0.13124513975412253
• License:
• Abstract: This paper investigates the industrial setting of real-time classification of early media exchanged during the initialization phase of voice calls. We explore the application of state-of-the-art audio tagging models and highlight some limitations when applied to the classification of early media. While most existing approaches leverage convolutional neural networks, we propose a novel approach for low-resource requirements based on gradient-boosted trees. Our approach not only demonstrates a substantial improvement in runtime performance, but also exhibits a comparable accuracy. We show that leveraging knowledge distillation and class aggregation techniques to train a simpler and smaller model accelerates the classification of early media in voice calls. We provide a detailed analysis of the results on a proprietary and publicly available dataset, regarding accuracy and runtime performance. We additionally report a case study of the achieved performance improvements at a regional data center in India.

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