Fusion of spectrograph and LPC analysis for word recognition: A new fuzzy approach
Reza Hoseinnezhad, Behzad Moshiri, Parisa Eslambolchilar · 2004
Word Recognition is generally difficult and imprecise if we use just one method. In this article, data fusion is applied to word recognition by integration of two features extracted form human speech: speech spectrograph and time domain features (spectral coefficients). Four different methods are applied to fusion of these features, including weighted averaging, k-means clustering, fuzzy k-means and fuzzy vector quantization. Simulation results show that fusion of time domain and spectrograph features yields more precise and satisfactory results compared to other methods of word recognition that use just one speech feature for word recognition, like FVQ/MLP (fuzzy vector quantization combined with multi-layered perceptron neural network). The importance of this result is prominent if the signal to noise ratio is low.