Improved Language Identification in Presence of Speech Coding

Automatically identifying the language being spoken from speech plays a vital role in operating multilingual speech processing applications. A rapid growth in the use of mobile communication devices has inflicted the necessity of operating all speech processing applications in mobile environments. Degradation in the performance of any speech processing applications is majorly due to varying background environments, speech coding and transmission errors. In this work, we focus on developing a language identification system robust to degradations in coding environments in Indian scenario. Spectral features (MFCC) extracted from high sonority regions of speech are used for language identification. Sonorant regions of speech are the regions of speech that are perceptually loud, carry a clear pitch. The quality of coded speech in high sonority region is high compared to less sonorant regions. Spectral features (MFCC) extracted from high sonority regions of speech are used for language identification. In this work, GMM-UBM based modelling technique is employed to develop an language identification (LID) system. Present study is carried out on IITKGP-MLILSC speech database.