Articulatory phonetic features for improved speech recognition

This thesis elaborates the use of speech production knowledge in the form of articulatory phonetic features to improve the robustness of speech recognition in practical situations. The main concept is that natural speech has three attributes in the human speech processing system, i.e., the motor activation, the articulatory trajectory, and the auditory perception. Consequently, the research work has three components. First, it describes an adaptive neural control model, which reproduces the articulatory trajectories and retrieves the motor activation patterns in a bio-mechanical speech synthesizer. Second, by manipulating the elastic vocal tract walls, the synthesizer produces the overall articulatory-to-acoustic trajectory map for English pronunciations. Third, the articulatory phonetic features are extracted in neural networks for speech recognition in cross-speaker and noisy conditions. The experimental results are compared with the traditional hidden Markov baseline system.Doctor of Philosophy (EEE