Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Abstract

Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vectors of open SMILE features of different duration utterances. Although it improves the SLID system’s accuracy for mismatched training and testing durations, the performance is significantly reduced for short-duration utterances. A cascade of inter-family and intra-family classifiers with an additional class to improve false language family estimation. All India Radio data set with nine Indian languages and different utterance durations was used as speech material. Experimental results showed that 150 optimal features with the proposed modified hierarchical classifier showed the highest accuracy of $$96.9\\%$$\n and $$84.4\\%$$\n for 30 s and 0.2 s utterances for the same train-test duration. However, we achieved an accuracy of $$98.3\\%$$\n and $$61.9\\%$$\n for 15 and 0.2 s test duration when trained with 30 s duration utterance. Comparative analysis showed a significant improvement in accuracy than several SLID systems in the literature.