Language Identification From Speech Features Using SVM and LDA

Speech based language identification system has a wide range of applications in the field of telephone services, multilingual translation services, government intelligence and monitoring etc. Identifying the exact speech feature for classification is an important problem in the language identification research area. In this work, we are comparing the performance measures of a language identification system using two different supervised learning algorithms. Mel frequency cepstral coefficients and formant feature vectors are extracted for classification purpose. The system which is developed using the database of seven different Indian languages is capable of identifying languages with LDA giving a maximum classification accuracy of 93.88% when compared to SVM with a classification accuracy of 84%.