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[[File:TDNN Diagram.png|thumb|right|TDNN diagram]]
'''Time delay neural network''' ('''TDNN''')<ref name="phoneme detection">{{cite journal |doi=10.1109/29.21701 |url=http://www.inf.ufrgs.br/~engel/data/media/file/cmp121/waibel89_TDNN.pdf |title=Phoneme recognition using time-delay neural networks |date=1989 |last1=Waibel |first1=A. |last2=Hanazawa |first2=T. |last3=Hinton |first3=G. |last4=Shikano |first4=K. |last5=Lang |first5=K.J. |journal=IEEE Transactions on Acoustics, Speech, and Signal Processing |volume=37 |issue=3 |pages=328–339 }}</ref> is a multilayer [[artificial neural network]] architecture whose purpose is to 1) classify patterns with shift-invariance, and 2) model context at each layer of the network. It is essentially a 1-d [[convolutional neural network]] (CNN) without pooling or dilations.
Shift-invariant classification means that the classifier does not require explicit segmentation prior to classification. For the classification of a temporal pattern (such as speech), the TDNN thus avoids having to determine the beginning and end points of sounds before classifying them.
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