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Volume 26 Issue 5
May  2004
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Luo Yu, Du Li-min. HMM Local Optimal State Path-Based Data Imputation[J]. Journal of Electronics & Information Technology, 2004, 26(5): 722-726.
Citation: Luo Yu, Du Li-min. HMM Local Optimal State Path-Based Data Imputation[J]. Journal of Electronics & Information Technology, 2004, 26(5): 722-726.

HMM Local Optimal State Path-Based Data Imputation

  • Received Date: 2003-02-27
  • Rev Recd Date: 2003-06-10
  • Publish Date: 2004-05-19
  • This paper presents a HMM Local Optimal state Path-based Data Imputation (LOPDI) algorithm. Speech feature vector sequences are presumed to be the outputs of an L state HMM. The HMM state sequence is estimated by local optimal path procedure. Then, missing data is recovered by MAP procedure. Experimental result shows that LOPDI algorithm can greatly increase automatic speech recognition systems robustness against additive noise.
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  • Morris A C.[J].Cooke M, Green P. Some solutions to the missing feature problem in data classification, with application to noise robust ASR. Proc. ICASSP98, Seattle.1998,:-[2]Vizinho A, Green P, Cooke M, Josifovski L. Missing data theory, spectral subtraction and signalto-noise estimation for robust ASR: An integrated study[J].Eurospeech99, Budapest.1999, vol.5:2407-2410[3]Cooke M, Green P, Josifovski L, Vizinho A. Robust automatic speech recognition with missing and unreliable acoustic data[J].Speech Communication.2001, 34(3):267-285[4]Raj B, Seltzer M, Stern R. Reconstruction of damaged spectrographic features for robust speech recognition[J].In Proceedings ICSLP00, Beijing, China, October.2000, vol.1:375-360[5]Raj B. Reconstruction of incomplete spectrograms for robust speech recognition. [Ph.D Thesis],ECE Department, Carnegie Mellon University, April, 2000.[6]Rabiner L R, Juang B H. Fundamentals of Speech Recognition. Prentice-Hall Press, 1993, Ch.6:321-389.[7]Steve Young, Dan Kershaw, Julian Odell, Dave Ollason, Valtcho Valtchev, Phil Woodland. The HTK Book (for HTK Version 3.0), Microsoft Corporation, 2000.
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