TY - GEN
T1 - Hidden Markov model and neural network hybrid
AU - Yook, Dongsuk
PY - 2002
Y1 - 2002
N2 - When there is a mismatch between training and testing environments, statistical pattern classification methods may suffer from severe degradation in their performance because the parameters in the classifiers do not represent the testing data well. The mismatch is typically due to the interference or noises from operating environments. In this paper, a neural network based transformation approach is studied to handle the distribution mismatches between training and testing data. The probability density functions of the statistical classifiers are used as the objective function of the neural network. The neural network maximizes the likelihood of the data from a testing environment, and allows global optimization of the network when used with the statistical pattern classifiers. The proposed approach is applied to the area of automatic speech recognition to recognize noisy distant-talking speech and it reduces the error rate by 52.9%.
AB - When there is a mismatch between training and testing environments, statistical pattern classification methods may suffer from severe degradation in their performance because the parameters in the classifiers do not represent the testing data well. The mismatch is typically due to the interference or noises from operating environments. In this paper, a neural network based transformation approach is studied to handle the distribution mismatches between training and testing data. The probability density functions of the statistical classifiers are used as the objective function of the neural network. The neural network maximizes the likelihood of the data from a testing environment, and allows global optimization of the network when used with the statistical pattern classifiers. The proposed approach is applied to the area of automatic speech recognition to recognize noisy distant-talking speech and it reduces the error rate by 52.9%.
UR - http://www.scopus.com/inward/record.url?scp=84896928614&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896928614&partnerID=8YFLogxK
U2 - 10.1007/3-540-36087-5_23
DO - 10.1007/3-540-36087-5_23
M3 - Conference contribution
AN - SCOPUS:84896928614
SN - 3540000283
SN - 9783540000280
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 203
BT - EurAsia-ICT 2002
A2 - Shafazand, Hassan
A2 - Tjoa, A. Min
A2 - Shafazand, Hassan
PB - Springer Verlag
T2 - 1st EurAsian Conference on Advances in Information and Communication Technology, EurAsia-ICT 2002
Y2 - 29 October 2002 through 31 October 2002
ER -