Language identification with neural networks: a feasibility study

Abstract

The feasibility of an approach to automatic language identification that combines recent advances in computer speech recognition and artificial neural networks is discussed. It is shown that artificial neural networks can be used as pattern classifiers that use information about distributions of broad phonetic categories to identify languages. Using artificial languages that differ only by their distribution of stop consonants, feature vectors were extracted from varying amounts of speech from each language. These feature vectors were then used to train an artificial neural network using the back-propagation algorithm. Classification results for two different sets of artificial languages are presented.<>