Speech Recognition for Air Traffic Communication

Abstract

This thesis deals with speech recognition. The aim is to build a speech recognition system based on neural networks and test it on recordings of air traffic communication. Final acoustic model will be used in project A-PiMod. The system reached word error rate 29.5%. Next task of this thesis was to experiment with neural networks which are part of acoustic model. First experiments explored its simplification and acceleration and its impact on error rate. Next experiments dealt with activation function rectifier and convolutional neural networks. Experiments with convolutional neural networks achieved 1.5% improvement, so the final result was 0.4% better than fully connected network with the same architecture