“…In the features based approaches, various types of fluency related features, e.g. statistics of speech breaks [6][7][8], speech rate [6][7][8][9][10], filled pauses [7] and goodness of pronunciation (GOP) [11,12], are computed from time stamps provided for input speech, e.g., beginning and end time of words, phonemes. Different machine learning models, e.g., support vector machine (SVM) [7,13] and feed-forward neural networks [8], were trained to predict fluency scores from the extracted features.…”