Pronunciation learning system for the 32 vowel system of Nasa Yuwe language

Nasa Yuwe is an indigenous language from Colombia (South America), it is, to some extent, an endangered language. Different efforts have been done to revitalize it, the most important of which being the unification of the Nasa Yuwe alphabet. The Nasa Yuwe vowel system has 32 vowels contrasting in nasalization, length, aspiration and glottalization, causing great confusion for the learner. In order to support the correct learning of this language, three classifier models (K-nearest neighbor, multilayer neural networks and Hidden Markov Model) have been developed to detect confusion in the pronunciation of the 32 vowels. They were developed in three different experiments in order to reach the best accuracy rates. The selected strategy developed binary classifiers using bagging with adding a number of negatives samples for each vowel, with an accuracy rate of about 85%. With these trained classifiers, a Computer Assisted Language Learning system prototype (CALL) was designed to support the correct pronunciation of the language’s vowels. Additionally using this system, the native and non-native speakers score distribution of acceptance was calculated and the confusion of vowels for non-native speaker corpus was evaluated.