Classroom speech intelligibility prediction system for front-rear speech amplified classroom based on audio features - Mohammad Ridhwan Tamjis (School of Mechatronic Engineering, 2012)
The goal of this research is to develop an intelligent speech intelligibility prediction system by combining audio-features (spectral rolloff (SR), spectral centroid (SC), power (PO), zero-crossings rate (ZCR), and short time energy (STE)) and classifiers (feed forward neural network (FFNN), Elman network (ENN)).