Badenhorst, JacoDavel, Marelie H.2018-03-022018-03-022015Jaco Badenhorst and Marelie Davel, “Synthetic triphones from trajectory-based feature distributions”, in Proc. a22, Port Elizabeth, South Africa, 2015. [http://engineering.nwu.ac.za/multilingual-speech-technologies-must/publications]http://ieeexplore.ieee.org/document/7359509/https://researchspace.csir.co.za/dspace/handle/10204/8737http://hdl.handle.net/10394/26487We 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.enSynthetic triphonesTrajectory modellingTrajectory-based featuresFeature distributionsFeature constructionPresentation