Alexander Waibel is a professor of Computer Science at Carnegie Mellon University and Karlsruhe Institute of Technology. Waibel's research interests focus on speech recognition and translation and human communication signals and systems. Alex Waibel made pioneering contributions to speech translation systems, breaking down language barriers through cross-lingual speech communication. In fundamental research on machine learning, he is known for the Time Delay Neural Network (TDNN), the first Convolutional Neural Network (CNN) trained by gradient descent, using backpropagation. Alex Waibel introduced the TDNN in 1987 at ATR in Japan.

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer, 10,000 weights would be required for processing an image sized 100 × 100 pixels. However, applying cascaded convolution kernels, only 25 neurons are required to process 5x5-sized tiles. Higher-layer features are extracted from wider context windows, compared to lower-layer features.