Time Delay Neural Network

Time-delay neural networks (TDNNs) are a type of neural network architecture designed to process sequential data by incorporating information from past time steps, enabling effective modeling of temporal dependencies. Current research focuses on improving TDNN efficiency and performance through architectural innovations like incorporating attention mechanisms, multi-scale feature extraction, and hybrid models combining TDNNs with other architectures such as recurrent neural networks (RNNs) and transformers. These advancements are driving improvements in various applications, including speaker verification, speech recognition, and signal processing tasks like video denoising and acoustic signal classification, where TDNNs demonstrate strong performance and efficiency gains compared to alternative methods.