文件列表:

hmm源代码-1.03\generate_seq.cc (1611, 1995-08-22)
hmm源代码-1.03\hmm.cc (30348, 1995-08-22)
hmm源代码-1.03\hmm.h (4145, 1995-08-22)
hmm源代码-1.03\Makefile (1167, 1995-08-22)
hmm源代码-1.03\random.h (301, 1995-08-22)
hmm源代码-1.03\test.hmm (138, 1995-08-21)
hmm源代码-1.03\test_hmm.cc (632, 1995-08-22)
hmm源代码-1.03\train_hmm.cc (1822, 1995-08-22)
Hmm.pdf (3829202, 2012-02-07)
hmm-1.03\Makefile (1167, 1995-08-22)
HMM的C语言实现\backward.cpp (1919, 2002-09-26)
HMM的C语言实现\baum.cpp (4045, 2002-09-26)
HMM的C语言实现\hmm.h (2280, 2002-09-26)
HMM的C语言实现\hmmrand.cpp (434, 2002-09-26)
HMM的C语言实现\hmmutils.cpp (4079, 2002-09-26)
HMM的C语言实现\nrutil.cpp (10821, 2002-09-26)
HMM的C语言实现\nrutil.h (1448, 2002-09-26)
HMM的C语言实现\viterbi.cpp (3020, 2002-09-26)
hmm源代码-1.03 (0, 2012-02-05)
hmm-1.03 (0, 2012-02-05)
HMM的C语言实现 (0, 2012-02-05)

H I D D E N M A R K O V M O D E L for automatic speech recognition 7/30/95 This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models. Jim and I built this code in order to learn how HMM systems work and we are now offering it to the net so that others can learn how to use HMMs for speech recognition. Keep in mind that efficiency was not our primary concern when we built this code, but ease of understanding was. I expect people to use this code in two different ways. People who wish to build an experimental speech recognition system can use the included "train_hmm" and "test_hmm" programs as black box components. The code can also be used in conjunction with written tutorials on HMMs to understand how they work. HOW TO COMPILE IT: We built this code on a Linux system (8meg RAM) and it has been tested under SunOS as well; it should run on any system with Gnu C++ and has been tested to be ANSI compliant. To compile and test the program, 1) extract the code: tar -xf hmm.tar 2) compile the programs: make all 3) create test sequences: generate_seq test.hmm 20 50 4) train using existing model: train_hmm test.hmm.seq test.hmm .01 5) train using random parameters: train_hmm test.hmm.seq 1234 3 3 .01 After steps 4 and 5 you can compare the file test.hmm.seq.hmm with test.hmm to confirm that the program is working. FILE FORMATS: There are two types of files used by these programs. The first is the hmm model file which has the following header: states: symbols: A series of ordered blocks follow the header, each of which is two lines long. Each block corresponds to a state in the model. The first line of each block gives the probability of the model recurring followed by the probability of generating each of the possible output symbols when it recurs. The second line gives the probability of the model transitioning to the next state followed by the probability of generating each of the possible output symbols when it transitions. The file "test.hmm" gives an example of this format for a three state model with three possible output symbols. The second kind of file is a listh of which) poss) le o) put ) mbol) ) e se) nd k) d of) ile ) a l) th o) whic) pos) le ) put) mbo) ) e s) nd ) d o) ile) a ) th ) whi) po) le) pu) mb) ) e ) nd) d ) il) a) th) wh) p) l) p) m) ) e) n) d) i) ) t) w) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )

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