Automatic rhythm modeling for language identification

This paper deals with an approach to Automatic Language Identification based on rhythmic modeling. Beside phonetics and phonotactics, rhythm is actually one of the most promising features to be considered for language identification, but significant problems are unresolved for its modeling. In this paper, an algorithm of rhythm extraction is described. Experiments are performed on read speech for 5 European languages. They show that salient features may be automatically extracted and efficiently modeled from the raw signal: a Gaussian mixture modeling of the extracted features results in a 81 % percent of correct language identification for the 5 languages, using 20 s duration utterances.