This is the results from two years of study whose overal achivement is an average assessment accuracy level of 72% for non-native adult speakers. The correlation between the human scores and the machine scores for an overall measure of speaking was 0.86 thus proving the reliability of the measure of speaking in tests.
- it transforms sounds/language into vectores in a n-dimension sphere in which each feature is vectorized to represent pronunciation, prosody, and language for further evaluation.
- The models range from parametric, non-parametric statistics to a Neural Networks architecture. The ETS scoring rubric philosophy was generally adopted for judgment on the spoken language proficiency level. This framework could change and be customized on demand.
- The mic input is crucial in the accuracy of the results. Certainly, pre-recorded sounds can be analysed, some parts of acoustic features which contain key information will be vanished though. It happens when the sound is compressed during the digitalization process.
- CART
- ETC
- NN
- LDA
- LR
- MLTRNL
- CNN
- RNN
- myspsolution
- NB
- PCA
- REF
- SVN
- SET-1; it was developed based on non-native English speakers who prepared for the TOEFL test
- SET-2; it was developed based on non-native and native English speakers in ordinary conversation situations.
- SET-3; it was developed based on non-native and native English speakers where they spoke about specific topics with having background knowledge of them.