PENGENALAN TUTUR KATA TERISOLASI MENGGUNAKAN MFCC DAN ANFIS - , UTIS SUTISNA; , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Automatic Speech Recognition has achieved substantial success in the past few decades but more studies are needed because none of the current methods are fast and precise enough to be comparable with human recognition abilities. In the area of voice signal processing, neural network system has been widely applied to speech recognition process. Implementation of neuro-fuzzy system that combine fuzzy system with neural network is possible to provide good results. In this study the Adaptive Neuro Fuzzy Inference System (ANFIS) is used for speech recognition in Indonesian with feature extraction using Mel-frequency cepstrum algorithm Coefficient (MFCC). Neuro-fuzzy system has two initial phases. The first phase is the system identification using Fuzzy C-Means (FCM) algorithm to identify and initialize the parameters of the rules used in fuzzy inference system (FIS). The second phase is fuzzy systems training using ANFIS to optimize the parameters of fuzzy system. The system implements variation of the number of fuzzy rule. System performance was evaluated using speech samples for training and testing. The test results using training data show that the highest average accuracy of recognition is 100%. Testing with test data yield the highest average accuracy rate of 89,33% with the number of fuzzy rules of 100.