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

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)
Number of pages4
Publication statusPublished - 2012

    Research areas

  • 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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