Combining fuzzy vector quantization and neural network classification for robust isolated word speech recognition
L. Cong, Costas S. Xydeas, A.F. Erwood · 2002
The issue of robust isolated word speech recognition in cases where the input signal is corrupted by acoustic noise, is addressed with a new fuzzy vector quantization (FVQ)/neural network scheme. The proposed system combines in a simple and effective way the fuzzy classification capability of FVQ with the non-linear pattern discrimination power of the multi-layer perception (MLP) neural network. The paper thus defines the design and algorithmic operation of this system and compares its recognition performance to that of a conventional FVQ/hidden Markov model (HMM) system. Computer simulation results obtained using speech corrupted by car or white noise indicate that FVQ/MLP provides significantly better performance than FVQ/HMM.>