Time delay neural network

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TDNN Diagram

Time delay neural network (TDNN) [1] is a artificial neural network architecture whose primary purpose is to work on sequential data. The TDNN units recognise features independent of time-shift (i.e. sequence position) and usually form part of a larger pattern recognition system. The main application is converting continuous audio into a stream of classified phoneme labels for speech recognition.

An input signal is augmented with delayed copies as other inputs, the neural network is time-shift invariant since it has no internal state.

The original paper presented a perceptron network whose connection weights were trained with the back-propagation algorithm, this may be done in batch or online. The Stuttgart Neural Network Simulator[2] implements that version.

References[edit]

  1. ^ Alexander Waibel et al, Phoneme Recognition Using Time-Delay Neural Networks IEEE Transactions on Acoustics, Speech and Signal Processing, Volume 37, No. 3, pp. 328. - 339 March 1989.
  2. ^ TDNN Fundamentals, Kapitel aus dem Online Handbuch des SNNS