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如何用kaldi做孤立词识别三

时间:2017-10-13 12:48:01      阅读:23      评论:0      收藏:0      [点我收藏+]

标签:nta   ima   mvn   lap   thread   识别   tin   extra   actor   

这次wer由15%下降到0%了,后面跑更多的模型

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LOG (apply-cmvn[5.2.124~1396-70748]:main():apply-cmvn.cc:162) Applied cepstral mean normalization to 20 utterances, errors on 0
200_001_001 espresso
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_001 is -9.06026 over 118 frames.
200_001_002 lungo
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_002 is -9.0791 over 87 frames.
200_001_003 extralungo
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_003 is -8.72467 over 121 frames.
200_001_004 cappuccino
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_004 is -9.11234 over 83 frames.
200_001_005 lattemakiato
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_005 is -9.0466 over 120 frames.
200_001_006 bluemountain
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_006 is -8.86214 over 116 frames.
200_001_007 ok
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_007 is -10.095 over 94 frames.
200_001_008 yes
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_008 is -9.39383 over 46 frames.
200_001_009 no
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_009 is -9.29525 over 68 frames.
200_001_010 thankyou
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_001_010 is -9.45605 over 73 frames.
200_002_001 espresso
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_001 is -8.823 over 99 frames.
200_002_002 lungo
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_002 is -8.86786 over 85 frames.
200_002_003 extralungo
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_003 is -9.15775 over 123 frames.
200_002_004 cappuccino
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_004 is -9.08465 over 75 frames.
200_002_005 lattemakiato
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_005 is -8.55999 over 117 frames.
200_002_006 bluemountain
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_006 is -9.36011 over 110 frames.
200_002_007 ok
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_007 is -9.99029 over 64 frames.
200_002_008 yes
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_008 is -9.46437 over 77 frames.
200_002_009 no
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_009 is -10.0669 over 51 frames.
200_002_010 thankyou
LOG (gmm-latgen-faster[5.2.124~1396-70748]:DecodeUtteranceLatticeFaster():decoder-wrappers.cc:286) Log-like per frame for utterance 200_002_010 is -9.69364 over 69 frames.
LOG (gmm-latgen-faster[5.2.124~1396-70748]:main():gmm-latgen-faster.cc:176) Time taken 0.457478s: real-time factor assuming 100 frames/sec is 0.0254721
LOG (gmm-latgen-faster[5.2.124~1396-70748]:main():gmm-latgen-faster.cc:179) Done 20 utterances, failed for 0
LOG (gmm-latgen-faster[5.2.124~1396-70748]:main():gmm-latgen-faster.cc:181) Overall log-likelihood per frame is -9.18962 over 1796 frames.
# Accounting: time=0 threads=1
# Ended (code 0) at Fri Oct 13 11:22:18 CST 2017, elapsed time 0 seconds

 

如何用kaldi做孤立词识别三

标签:nta   ima   mvn   lap   thread   识别   tin   extra   actor   

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