@conference{287,
  keywords = {synthetic triphones, trajectory modelling, trajectory-based features, feature distributions, feature construction},
  author = {Jaco Badenhorst and Marelie Davel},
  title = {Synthetic triphones from trajectory-based feature distributions},
  abstract = {We experiment with a new method to create synthetic models of rare and unseen triphones in order to supplement limited automatic speech recognition (ASR) training data. A trajectory model is used to characterise seen transitions at the spectral level, and these models are then used to create features for unseen or rare triphones. We find that a fairly restricted model (piece-wise linear with three line segments per channel of a diphone transition) is able to represent training data quite accurately. We report on initial results when creating additional triphones for a single-speaker data set, finding small but significant gains, especially when adding additional samples of rare (rather than unseen) triphones.},
  year = {2015},
  journal = {Pattern Recognition Association of South Africa (PRASA)},
  chapter = {118-122},
  address = {Port Elizabeth, South Africa},
  isbn = { 978-1-4673-7450-7, 978-1-4673-7449-1},
  doi = {10.1109/RoboMech.2015.7359509},
}