Аннотация
The research presented in this article focuses on the development of a system for detecting speech activity in audio streams using convolutional neural networks (CNNs). Speech activity detection plays a crucial role in many modern applications, such as voice-activated assistants, real-time communication platforms, and automated transcription services. The study synthesizes findings from nine key studies, demonstrating the effectiveness of CNNs in handling complex audio data, isolating speech signals from noise, and improving overall detection accuracy.The research emphasizes the architectural advantages of deep CNN models, such as VGG, ResNet, and AlexNet, highlighting their ability to capture intricate audio features and improve performance across various environments. The study also explores techniques like data augmentation and optimization algorithms, which further enhance the robustness and efficiency of these models.By evaluating the effectiveness of different CNN architectures and comparing various evaluation metrics, the research identifies potential areas for future exploration, such as optimizing CNN models for real-time applications and exploring hybrid architectures. Overall, this research offers valuable insights into the state of CNN-based speech activity detection and its implications for real-world applications.