Dual-Discriminability-Analysis Type-2 Fuzzy-Neural-Network Based Speech Classification for Human-Machine Interaction

Speech detection and speech recognition are two important classification problems in human-robot interaction. They are easily affected by the noisy environment. To solve this problem, a dual-discriminability-analysis Type-2 fuzzy neural network (DDA2FNN) is proposed. In handing problems with uncertainties such as noisy data, Type-2 fuzzysystems generally outperform their Type-1 counterparts. Hence, Type-2 fuzzy-sets are adopted in the antecedent parts to model the noisy data. To enhance the “discriminability”, a dual-discriminability-analysis (DDA) method is proposed in the consequent parts. The novelty of DDA is its consideration of both linear-discriminant-analysis (LDA) and minimum-classification-error (MCE). The proposed dual-discriminability-analysis Type2 fuzzy rule includes an LDA-matrix and an MCE-matrix. Compared with other existing fuzzy neural networks, the novelty of the proposed DDA2FNN is its consideration of both uncertainty and discriminability. The effectiveness of the proposed DDA2FNN is demonstrated by two speech classification problems. Experimental results and theoretical analysis indicate that the proposed DDA2FNN performs better than the other fuzzy neural networks.