Explainable AI Models for Voice-Based Content Classification in Streaming Television Platforms - Saraschandra Arveti (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The swift growth of streaming television platforms has made it possible to have a tremendous amount of audiovisual content that needs to be classified efficiently and transparently for recommending, moderating, and making the content accessible. In this paper, we put forward an explainable artificial intelligence (XAI) system for voice, based content classification that detects categories of programs by using speech signals extracted from streaming media. Our framework combines deep audio feature extraction with interpretable machine learning techniques that result in both excellent prediction performance and transparency of the decision, making process. Speech fragments are initially handled with the Mel, frequency cepstral coefficients (MFCCs) and spectral embeddings. These are later given to a hybrid structure that integrates a convolutional neural network (CNN) and a transformer, based attention layer. To strengthen the interpretability, the SHAP, driven explanation parts emphasize the most significant acoustic features and temporal voice patterns that have a great impact on classification decisions. The tool is test on a multi, genre streaming dataset comprising news sports entertainment, documentaries, and advertisements. The experimental results show excellent results and interpretability gains. The suggested model obtains 92.6% classification accuracy, which is 11.4% higher than that of baseline audio classifiers. The explainability module correctly detects essential speech signals with 89% explanation consistency. Furthermore, inference latency is cut by 27%, making near real, time deployment possible. Moreover. This model helps to improve genre recommendation precision by 18% in simulated streaming situations.