CMU Sphinx / Forums / Help: Did I successfully trained acoustic model

I did all the steps to train an acoutic model
After run

sphinxtrain run

I got these logs

Sphinxtrain path: /usr/local/lib/sphinxtrain
Sphinxtrain binaries path: /usr/local/libexec/sphinxtrain
Running the training
MODULE: 000 Computing feature from audio files
Extracting features from  segments starting at  (part 1 of 1) 
Extracting features from  segments starting at  (part 1 of 1) 
Feature extraction is done
MODULE: 00 verify training files
    Phase 1: Checking to see if the dict and filler dict agrees with the phonelist file.
        Found 133 words using 34 phones
    Phase 2: Checking to make sure there are not duplicate entries in the dictionary
    Phase 3: Check general format for the fileids file; utterance length (must be positive); files exist
    Phase 4: Checking number of lines in the transcript file should match lines in fileids file
    Phase 5: Determine amount of training data, see if n_tied_states seems reasonable.
        Estimated Total Hours Training: 0.702463888888889
        This is a small amount of data, no comment at this time
    Phase 6: Checking that all the words in the transcript are in the dictionary
        Words in dictionary: 130
        Words in filler dictionary: 3
    Phase 7: Checking that all the phones in the transcript are in the phonelist, and all phones in the phonelist appear at least once
MODULE: 0000 train grapheme-to-phoneme model
Skipped (set $CFG_G2P_MODEL = 'yes' to enable)
MODULE: 01 Train LDA transformation
Skipped (set $CFG_LDA_MLLT = 'yes' to enable)
MODULE: 02 Train MLLT transformation
Skipped (set $CFG_LDA_MLLT = 'yes' to enable)
MODULE: 05 Vector Quantization
Skipped for continuous models
MODULE: 10 Training Context Independent models for forced alignment and VTLN
Skipped:  $ST::CFG_FORCEDALIGN set to 'no' in sphinx_train.cfg
Skipped:  $ST::CFG_VTLN set to 'no' in sphinx_train.cfg
MODULE: 11 Force-aligning transcripts
Skipped:  $ST::CFG_FORCEDALIGN set to 'no' in sphinx_train.cfg
MODULE: 12 Force-aligning data for VTLN
Skipped:  $ST::CFG_VTLN set to 'no' in sphinx_train.cfg
MODULE: 20 Training Context Independent models
    Phase 1: Cleaning up directories:
    accumulator...logs...qmanager...models...
    Phase 2: Flat initialize
    Phase 3: Forward-Backward
        Baum welch starting for 1 Gaussian(s), iteration: 1 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -156.043331606607
        Baum welch starting for 1 Gaussian(s), iteration: 2 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -154.047697192817
        Convergence Ratio = 1.99563441378967
        Baum welch starting for 1 Gaussian(s), iteration: 3 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -151.482163970469
        Convergence Ratio = 2.56553322234797
        Baum welch starting for 1 Gaussian(s), iteration: 4 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -149.963659658266
        Convergence Ratio = 1.51850431220265
        Baum welch starting for 1 Gaussian(s), iteration: 5 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 5
        Current Overall Likelihood Per Frame = -149.573999454302
        Convergence Ratio = 0.389660203964297
        Baum welch starting for 1 Gaussian(s), iteration: 6 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 6
        Current Overall Likelihood Per Frame = -149.436428127978
        Convergence Ratio = 0.137571326323894
        Baum welch starting for 1 Gaussian(s), iteration: 7 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 7
        Current Overall Likelihood Per Frame = -149.319221628633
        Convergence Ratio = 0.117206499345428
        Baum welch starting for 1 Gaussian(s), iteration: 8 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 8
        Current Overall Likelihood Per Frame = -149.274023575747
        Training completed after 8 iterations
MODULE: 30 Training Context Dependent models
    Phase 1: Cleaning up directories:
    accumulator...logs...qmanager...
    Phase 2: Initialization
    Phase 3: Forward-Backward
        Baum welch starting for iteration: 1 (1 of 1) 
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -149.250178933674
        Baum welch starting for iteration: 2 (1 of 1) 
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -146.328755531127
        Convergence Ratio = 2.92142340254745
        Baum welch starting for iteration: 3 (1 of 1) 
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -145.397035039365
        Convergence Ratio = 0.931720491761581
        Baum welch starting for iteration: 4 (1 of 1) 
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -145.212011689015
        Convergence Ratio = 0.185023350349752
        Baum welch starting for iteration: 5 (1 of 1) 
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 5
        Current Overall Likelihood Per Frame = -145.152419855509
        Training completed after 5 iterations
MODULE: 40 Build Trees
    Phase 1: Cleaning up old log files...
    Phase 2: Make Questions
    Phase 3: Tree building
        Processing each phone with each state
        AA 0 
        AA 1 
        AA 2 
        AE 0 
        AE 1 
        AE 2 
        AH 0 
        AH 1 
        AH 2 
        AO 0 
        AO 1 
        AO 2 
        AW 0 
        AW 1 
        AW 2 
        AY 0 
        AY 1 
        AY 2 
        B 0 
        B 1 
        B 2 
        CH 0 
        CH 1 
        CH 2 
        D 0 
        D 1 
        D 2 
        EH 0 
        EH 1 
        EH 2 
        ER 0 
        ER 1 
        ER 2 
        EY 0 
        EY 1 
        EY 2 
        F 0 
        F 1 
        F 2 
        G 0 
        G 1 
        G 2 
        HH 0 
        HH 1 
        HH 2 
        IH 0 
        IH 1 
        IH 2 
        IY 0 
        IY 1 
        IY 2 
        JH 0 
        JH 1 
        JH 2 
        K 0 
        K 1 
        K 2 
        L 0 
        L 1 
        L 2 
        M 0 
        M 1 
        M 2 
        N 0 
        N 1 
        N 2 
        OW 0 
        OW 1 
        OW 2 
        P 0 
        P 1 
        P 2 
        R 0 
        R 1 
        R 2 
        S 0 
        S 1 
        S 2 
        Skipping SIL
        T 0 
        T 1 
        T 2 
        TH 0 
        TH 1 
        TH 2 
        UW 0 
        UW 1 
        UW 2 
        V 0 
        V 1 
        V 2 
        W 0 
        W 1 
        W 2 
        Y 0 
        Y 1 
        Y 2 
        Z 0 
        Z 1 
        Z 2 
MODULE: 45 Prune Trees
    Phase 1: Tree Pruning
    Phase 2: State Tying
MODULE: 50 Training Context dependent models
    Phase 1: Cleaning up directories:
    accumulator...logs...qmanager...
    Phase 2: Copy CI to CD initialize
    Phase 3: Forward-Backward
        Baum welch starting for 1 Gaussian(s), iteration: 1 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -149.250178933674
        Baum welch starting for 1 Gaussian(s), iteration: 2 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -148.399957293178
        Convergence Ratio = 0.850221640495619
        Baum welch starting for 1 Gaussian(s), iteration: 3 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -148.271560024833
        Convergence Ratio = 0.12839726834477
        Baum welch starting for 1 Gaussian(s), iteration: 4 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
ERROR: This step had 2 ERROR messages and 0 WARNING messages.  Please check the log file for details.
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -148.222032099118
        Split Gaussians, increase by 1
        Current Overall Likelihood Per Frame = -148.222032099118
        Convergence Ratio = 0.0495279257154948
        Baum welch starting for 2 Gaussian(s), iteration: 1 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
ERROR: This step had 2 ERROR messages and 0 WARNING messages.  Please check the log file for details.
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -148.641055500103
        Baum welch starting for 2 Gaussian(s), iteration: 2 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
ERROR: This step had 2 ERROR messages and 0 WARNING messages.  Please check the log file for details.
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -148.092589951994
        Convergence Ratio = 0.548465548109078
        Baum welch starting for 2 Gaussian(s), iteration: 3 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
ERROR: This step had 2 ERROR messages and 0 WARNING messages.  Please check the log file for details.
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -147.705530997988
        Convergence Ratio = 0.387058954006136
        Baum welch starting for 2 Gaussian(s), iteration: 4 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
ERROR: This step had 2 ERROR messages and 0 WARNING messages.  Please check the log file for details.
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -147.292369726064
        Convergence Ratio = 0.413161271923713
        Baum welch starting for 2 Gaussian(s), iteration: 5 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 5
        Current Overall Likelihood Per Frame = -147.064064186771
        Convergence Ratio = 0.228305539292847
        Baum welch starting for 2 Gaussian(s), iteration: 6 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 6
        Current Overall Likelihood Per Frame = -146.951246999648
        Convergence Ratio = 0.112817187122914
        Baum welch starting for 2 Gaussian(s), iteration: 7 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 7
        Current Overall Likelihood Per Frame = -146.882006587923
        Split Gaussians, increase by 2
        Current Overall Likelihood Per Frame = -146.882006587923
        Convergence Ratio = 0.0692404117253318
        Baum welch starting for 4 Gaussian(s), iteration: 1 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -147.26968962422
        Baum welch starting for 4 Gaussian(s), iteration: 2 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -146.729250613911
        Convergence Ratio = 0.540439010309456
        Baum welch starting for 4 Gaussian(s), iteration: 3 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -146.475856805609
        Convergence Ratio = 0.253393808302178
        Baum welch starting for 4 Gaussian(s), iteration: 4 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -146.186518089107
        Convergence Ratio = 0.289338716502016
        Baum welch starting for 4 Gaussian(s), iteration: 5 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 5
        Current Overall Likelihood Per Frame = -145.993111547845
        Convergence Ratio = 0.193406541261538
        Baum welch starting for 4 Gaussian(s), iteration: 6 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 6
        Current Overall Likelihood Per Frame = -145.873967424186
        Convergence Ratio = 0.119144123659481
        Baum welch starting for 4 Gaussian(s), iteration: 7 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 7
        Current Overall Likelihood Per Frame = -145.798518705983
        Split Gaussians, increase by 4
        Current Overall Likelihood Per Frame = -145.798518705983
        Convergence Ratio = 0.0754487182026935
        Baum welch starting for 8 Gaussian(s), iteration: 1 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 1
        Current Overall Likelihood Per Frame = -146.177027684302
        Baum welch starting for 8 Gaussian(s), iteration: 2 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 2
        Current Overall Likelihood Per Frame = -145.625042014813
        Convergence Ratio = 0.551985669489056
        Baum welch starting for 8 Gaussian(s), iteration: 3 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 3
        Current Overall Likelihood Per Frame = -145.383629842578
        Convergence Ratio = 0.241412172235101
        Baum welch starting for 8 Gaussian(s), iteration: 4 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 4
        Current Overall Likelihood Per Frame = -145.13735383788
        Convergence Ratio = 0.246276004697819
        Baum welch starting for 8 Gaussian(s), iteration: 5 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 5
        Current Overall Likelihood Per Frame = -144.974949285649
        Convergence Ratio = 0.162404552230669
        Baum welch starting for 8 Gaussian(s), iteration: 6 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 6
        Current Overall Likelihood Per Frame = -144.87466734154
        Convergence Ratio = 0.100281944109099
        Baum welch starting for 8 Gaussian(s), iteration: 7 (1 of 1)
        0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 
        Normalization for iteration: 7
        Current Overall Likelihood Per Frame = -144.806692317122
Training for 8 Gaussian(s) completed after 7 iterations
MODULE: 60 Lattice Generation
Skipped:  $ST::CFG_MMIE set to 'no' in sphinx_train.cfg
MODULE: 61 Lattice Pruning
Skipped:  $ST::CFG_MMIE set to 'no' in sphinx_train.cfg
MODULE: 62 Lattice Format Conversion
Skipped:  $ST::CFG_MMIE set to 'no' in sphinx_train.cfg
MODULE: 65 MMIE Training
Skipped:  $ST::CFG_MMIE set to 'no' in sphinx_train.cfg
MODULE: 90 deleted interpolation
Skipped for continuous models
MODULE: DECODE Decoding using models previously trained
        Decoding 130 segments starting at 0 (part 1 of 1) 
        0% 
        Aligning results to find error rate
        SENTENCE ERROR: 46.2% (60/130)   WORD ERROR RATE: 15.5% (119/773)

Is this command run successfully ?
beacuse in logs ERROR: This step had 2 ERROR messages and 0 WARNING messages. Please check the log file for details.

Thanks in advance

Did I successfully trained acoustic model

Speech Recognition Toolkit

Forums

Help

Did I successfully trained acoustic model document.SUBSCRIPTION_OPTIONS = { "thing": "topic", "subscribed": false, "url": "subscribe", "icon": { "css": "fa fa-envelope-o" } };

Did I successfully trained acoustic model