@article{ETD,
      school = {Ph.D.},
      author = {Dinh, Tuan A.},
      url = {http://digitalcollections.ohsu.edu/record/9298},
      title = {Improving speech intelligibility through spectral style  conversion},
      publisher = {Oregon Health and Science University},
      abstract = {Oral communication is the most important way for  delivering information in our daily life. Unfortu-nately,  the quality of such communication can be degraded by 1)  speech disorders (e.g. dysarthria) and 2) surrounding  environments (e.g. noise or reverberation). Style  conversion is a technology that modifies the source  speaking style of a speaker to sound like a more  intelligible target speak-ing style of either the same or  different speaker. In the dissertation, I consider new  machine learning based-approaches for style conversion.  Inspired by the intelligibility gain of clear (CLR)  speaking style over habitual (HAB) speaking style, I  propose several HAB-to-CLR spectral mappings approaches for  intelligibility improvement.},
      number = {ETD},
      doi = {https://doi.org/10.6083/xk81jm08r},
      recid = {9298},
      address = {2021},
}