Maximum a posteriori adaptation of subspace Gaussian mixture models for cross-lingual speech recognition

Liang Lu, Arnab Ghoshal, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4877-4880
Number of pages4
DOIs
Publication statusPublished - 2012

Keywords

  • Gaussian processes
  • hidden Markov models
  • maximum likelihood estimation
  • speech recognition
  • GlobalPhone corpus
  • HMM-GMM
  • MAP adaptation approach
  • SGMM
  • cross-lingual acoustic modeling
  • cross-lingual baseline systems
  • cross-lingual speech recognition
  • global shared parameter estimation
  • maximum a posteriori adaptation
  • out-of-language training data
  • subspace Gaussian mixture models
  • target language resources
  • word error rate reductions
  • Acoustics
  • Adaptation models
  • Covariance matrix
  • Hidden Markov models
  • Mathematical model
  • Speech recognition
  • Training data
  • Cross-lingual Speech Recognition
  • Maximum a Posteriori Adaptation
  • Subspace Gaussian Mixture Model

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