September 22-25, 1997
In this paper, an empirical comparison of two multilayer perceptron (MLP)-based techniques for key- word speech recognition (wordspotting) is described. The techniques are the predictive neural model (PNM)-based wordspotting, in which the MLP is applied as a speech pattern predictor to compute a local distance between the acoustic vector and the phone model, and the hybrid HMM/MLP-based wordspotting, where the MLP is used as a state (phone) probability estimator given acoustic vectors. The comparison was performed with the same database. According to our experiments, the hybrid HMM/MLP-based technique excels the PNM-based techniques (~6.2 %).
Bibliographic reference. Suhardi / Fellbaum, Klaus (1997): "Empirical comparison of two multilayer perceptron-based keyword speech recognition algorithms", In EUROSPEECH-1997, 2835-2838.