human
summarize: The authors of ​[32]​ suggested a factorized version of the TDNN layer and termed it TDNN-F, which obtained the same performance as TDNN+LSTM neural network topologies. A TDNN-F layer is like a TDNN layer, but it is compressed using singular value decomposition (SVD) ​[33]​, reducing the number of training parameters. When training a TDNN-F layer using random weights, one of the two elements of each matrix must be semi-orthogonal while applying SVD to all layers. Figure 13 illustrates the difference between TDNN and TDNN-f. 
gpt
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open