State Level Control for Acoustic Model Training

  • German Chernykh
  • Maxim Korenevsky
  • Kirill Levin
  • Irina Ponomareva
  • Natalia Tomashenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8773)


We propose a method for controlling Gaussian mixture splitting in HMM states during the training of acoustic models. The method is based on introducing special criteria of mixture quality in every state. These criteria are calculated over a separate part of the speech database. We back up states before splitting and revert to saved copies of the states whose criteria values have decreased, which makes it possible to optimize the number of Gaussians in the GMMs of the states and to prevent overfitting. The models obtained by such training demonstrate improved recognition rate with a significantly smaller number of Gaussians per state.


acoustic modeling GMM-HMM models Gaussian splitting 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • German Chernykh
    • 1
  • Maxim Korenevsky
    • 2
  • Kirill Levin
    • 2
  • Irina Ponomareva
    • 2
  • Natalia Tomashenko
    • 2
    • 3
  1. 1.Dept. of PhysicsSaint-Petersburg State UniversitySaint-PetersburgRussia
  2. 2.Speech Technology CenterSaint-PetersburgRussia
  3. 3.Dept. of Speech Information SystemsITMO UniversitySaint-PetersburgRussia

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