Automatic text independent language identification using reduct set of feature vectors
MORE SADANANDAM, V. Kamakshi Prasad · 2013
In this paper, robust features are proposed for spoken language identification (LID) system. 12 Mel frequency cepstral coefficients (MFCCs) and five formant frequencies are extracted from each short-time windowed speech signal. These features are concatenated to form 17-dimensional feature vectors. 8-dimensional reduct set is obtained from this 17-dimensional feature vector using rough set theory. This 8-dimensional reduct set is transformed into 15 dimensional new feature vectors using the approach followed in [1]. In both the training and testing phases of LID, these 15 dimensional feature vectors are used. Due to usage of reduct set, there is a significant reduction of time in training and testing phases of the proposed LID system. The experiments are carried out on speech database of Indian languages and the results are impressive.