TDNN

TDNN, short for time-delay neural network, is a feed-forward neural network designed for sequence data. Instead of processing one time step at a time with recurrence, a TDNN explicitly models temporal context by feeding each neuron inputs from multiple time-delayed copies of the input vector. The connections are organized in taps across time, with shared weights across time steps, enabling the network to learn temporal patterns within a fixed window.

Typically, a TDNN consists of several layers, each applying affine transformations to the concatenation of input frames at specified delays (for example t-2, t, t+2). Nonlinear activation follows. Some architectures use multiple branches with different delay sets to capture different timescales, akin to a multi-branch 1D convolution along the time axis.

TDNNs were introduced in the 1980s by Waibel and colleagues for phoneme recognition and speech processing. They provided an alternative to recurrent networks by offering efficient parallel training and straightforward gradient flow. Over time, TDNNs influenced modern temporal convolutional techniques and were incorporated into large-scale speech systems, including Kaldi, sometimes in modified forms such as TDNN-F (factorized TDNN) that improve parameter efficiency.

Applications include speech recognition, speaker verification, phoneme classification, and other sequence modeling tasks where temporal context matters. Limitations include fixed context length, which may miss longer-range dependencies, and potentially larger parameter counts with respect to long sequences. They are often outperformed by more flexible recurrent or attention-based models on tasks requiring long-range context but remain efficient and easy to train for moderate context windows.

Related concepts: temporal convolution, 1D convolution, dilated convolution, recurrent neural networks, convolutional neural networks.