Merging segmental and rhythmic features for Automatic Language Identification
Jérôme Farinas, François Pellegrino, Jean-Luc Rouas, Régine Andre-Obrecht · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
This paper deals with an approach to Automatic Language Identification based on rhythmic modeling and vowel system modeling. Experiments are performed on read speech for 5 European languages. They show that rhythm and stress may be automatically extracted and are relevant in language identification: using cross-validation, 78% of correct identification is reached with 21 s. utterances. The Vowel System Modeling, tested in the same conditions (cross-validation), is efficient and results in a 70% of correct identification for the 21 s. utterances. Last. merging the two models slightly improves the results.