Title :
Fuzzy Hidden Markov Models and fuzzy NN Models in Speaker Recognition
Author :
Taheri, Asghar ; Tarihi, Mohammad Reza ; Ali, Hadi Vafadar
Author_Institution :
Malik Ashtar Technol. Univ.
Abstract :
The fuzzy HMM algorithm is regarded as an application of the fuzzy expectation-maximization (EM) algorithm to the Baum-Welch algorithm in the HMM. The Texas Instruments p4 used speech and speaker recognition experiments and show better results for fuzzy HMMs compared with conventional HMMs. Equation and how estimation of discrete and continuous HMM parameters on based this two algorithm is explained and performance of two speech recognition method for one hundred is surveyed. This paper show better results for the fuzzy HMM, compared with the conventional HMM. After of that work we use fuzzy-neural network system was proposed for Farsi speech recognition. Instead of using the fuzzy membership input with class membership desired-output during training procedure as proposed by several researches, we used the fuzzy membership input with fundamental binary desired-output. This can reduce the misunderstood training, decrease the training time and also improve the recognition ability
Keywords :
fuzzy neural nets; hidden Markov models; speaker recognition; Baum-Welch algorithm; Farsi speech recognition; Texas Instruments p4; fuzzy NN models; fuzzy expectation-maximization algorithm; fuzzy hidden Markov models; fuzzy-neural network system; speaker recognition; Clustering algorithms; Fuzzy neural networks; Fuzzy systems; Hidden Markov models; Humans; Instruments; Neural networks; Speaker recognition; Speech recognition; Vector quantization;
Conference_Titel :
Industrial Electronics and Applications, 2006 1ST IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-9513-1
Electronic_ISBN :
0-7803-9514-X
DOI :
10.1109/ICIEA.2006.257377