Edinburgh Research Explorer

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)
Pages4877-4880
Number of pages4
DOIs
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

Download statistics

No data available

ID: 12330836