Paper data
Title:
Merging Segmental, Rhythmic and Fundamental Frequency Features for Automatic Language Identification Author(s): Rouas Jean-Luc, IRIT - UMR 5505 CNRS INPT UPS, France Farinas Jérôme, IRIT - UMR 5505 CNRS INPT UPS, France Pellegrino François, DDL - UMR 5596 CNRS Univ. Lyon 2, France Page numbers in the proceedings: Volume III pp 591-594 Session: Language and Speech Recognition
Paper abstract
This paper deals with an approach to Automatic Language Identification based on rhythmic and fundamental frequency modeling. Experiments are performed on read speech for 5 European languages. They show that rhythm can be automatically extracted and is relevant in language identification: using cross-validation, 79% of correct identification is reached with 21 s. utterances. The fundamental frequency modeling, tested in the same conditions (cross-validation), produces 50% of correct identification for the 21 s. utterances. The Vowel System Modeling gives an identification rate of 70% for the 21 s. utterances. Last, merging the three models slightly improves the identification rate.
Paper
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