Perception of speech signals using self-organization on linear neuron array

A continuous speech recognition system with finite set of Chinese words is devised for selected applications. With proper design of the self-organizing map for the speech signals, the precedence relations among the spectral patterns within a token period can be preserved by the topology preservations and the serious nonlinear time warping can thus be overcome. The 1D hierarchical relations among the sequential spectral patterns can be represented by the topology map developed on the linear array of neurons. We then devise two kinds of perception energies based on the trained map. One of the energies is derived from properly fitting a precedence curve on the sequential excitation patterns of the map during a whole word period. The other energy is obtained from the accumulation of total excitations on the map during a word period. Thresholds for the perception energies are then designed experimentally. A set of 1309 linear array maps are used for representing the total 1309 standard Chinese word pronunciations. Each linear array contains 100 equally spaced and linearly ordered neurons.