Phoneme Recognition: Neural Networks vs
Alexander H. Waibel, Hanazawa G. Hinton, IC Shikano Ic · 1988
neme recognition which is characterized by two important properties: 1.) Using a 3 layer arrangement of simple computing units, it can represent arbitrary nonlinear decision surfaces. The TDNN learns these decision surfaces automatically using error back-propagatioii[l]. 2.) he time-delay arrangement enables the network to discover acoustichonetic features and the temporal relationships between them indeendent of position in time and hence not blurred by temporal shifts in the input. For comparison, several discrete Hidden Markov Models (HMM) were trained to perform the same task, i.e., the speakerdependent recognition of the phonemes B, D, and G extracted We show that the TDNN invented well-known acoustic-phonetic