Development of a speech recognition-based automatic reading tutor for non-native children learners of the Filipino language - Bayona, Michael Gringo Angelo R. (College of Engineering, University of the Philippines Diliman, 2013)
Several implementations of language learning systems that use speech recognition have been implemented commercially, but not for the Filipino language. In this research, a reading tutor for the Filipino language for non-native children learners was developed. The reading tutor combines a speech recognizer with a reading miscue detector to decode the spoken input and identify any reading errors present in the utterance. Recognition experiments focused on the effect of vocal tract length normalization (VTLN) on systems trained on children's speech and on adult speech as well. Results showed that children's speech-trained models perform generally better, but adult speech-trained models may be a viable alternative when used together with VTLN. The classifier was implemented using logistic regression to decode errors at the word level. Comparisons were made among a forced alignment-based classifier, a speech recognition-based classifier, and a hybrid system that combines features from the other two systems. The miscue detection and false alarm rates were used to evaluate each systems's performance. Experiments showed that the forced alignment-based classifier using linear and quadratic features performed as well as the speech recognition-based classifier using linear features. The hybrid system outperforms the other two systems at false alarm rates of 5% and 10% with miscue detection rates of 47% and 62% respectively.