Title :
Study on a speech learning approach based on interval support vector regression
Author_Institution :
Dept. of Comput., North China Electr. Power Univ., Baoding, China
Abstract :
In this paper an interval regression model has been established to cope with the situation that the input training data is accurate while the output one is interval. And the model has been applied in English speech learning system to predict the credible interval of correct speech and then give a correct judgment for the learners. Experimental data show that the new model reduces the workload of fuzzy prediction and has good accuracy, so it can be effective in speech learning system.
Keywords :
computer aided instruction; eigenvalues and eigenfunctions; fuzzy set theory; learning (artificial intelligence); linguistics; regression analysis; speech recognition; speech synthesis; support vector machines; CALL system; English speech learning system; computer-assisted language learning system; eigenvector extraction; fuzzy prediction; interval support vector regression model; speech recognition; speech synthesis; training data; Computer science; Computer science education; Learning systems; Linear regression; Power system modeling; Predictive models; Speech recognition; Support vector machine classification; Support vector machines; Training data; SVR; eigenvector extraction; interval regression; speech learning;
Conference_Titel :
Computer Science & Education, 2009. ICCSE '09. 4th International Conference on
Conference_Location :
Nanning
Print_ISBN :
978-1-4244-3520-3
Electronic_ISBN :
978-1-4244-3521-0
DOI :
10.1109/ICCSE.2009.5228399
