Abstract
Transfer phenomena between Portuguese (L1) and English (L2) produced by Brazilian learners are well documented in the literature. However, the identification and classification of these processes are made mainly through transcriptions, a slow and laborious process done by specialized linguists. The rapid identification of these phenomena would be of great value for software doing proficiency placement tests and could be used in language schools, distance education, computer-assisted pronunciation training (CAPT) or by autodidacts and researchers. The present work analyzed possible techniques and tools that can be used in the automatic identification of some transfer processes. The data for the grapho-phonic-phonological transfer were synthetically generated in the Google Translate™ TTS system. Then we tested three classification algorithms to perform the identification: k-Nearest Neighbor, Centroid Minimum Distance, and Artificial Neural Networks. The results indicate that these techniques are of great value for Linguistics and for new software applications in language learning.
Highlights
Pronunciation is one of the key elements that influence the mastery of a language
It is plausible to predict that these formants, F1 and F2, carry information that characterizes the vowels produced by the Google TranslateTM TTS system in a level of detail that it is possible to identify the transfers from Brazilian Portuguese (BP) to English-L2, since F1 and F2 are used by the human brain to determine vowel spectral quality and distinguish between vowels (F1 is related to vowel height and F2 to tongue advancement)
It is defined as the number of true positives (TP) divided by the sum of true positives and false positives (FP)
Summary
Pronunciation is one of the key elements that influence the mastery of a language. Especially in the process of learning a non-native language (L2), pronunciation is a central concern for those who want to communicate effectively. Students in the process of learning an L2 transfer some of their knowledge of the L1 to the new language due to the already established structure of the L1, which might jeopardize communication at times This phenomenon, when manifested in speech or oral reading, is called grapho-phonic-phonological knowledge transfer [2]. There is a vast literature for grapho-phonic-phonological transfer between Brazilian Portuguese and English as a Foreign Language [11], there is a shortage of works aimed at recognizing and classifying these processes in an automated way. The first hypothesis we assumed was that the Google TranslateTM text-to-speech system is able to simulate the grapho-phonic-phonological transfer phenomena This way it would be possible to synthetically compose the dataset needed for the classification without any human tests in this first phase. It would be possible to create systems capable of doing the identification task but still maintaining simplicity and low processing power, ideal for online and mobile applications
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