LANGUAGE IDENTIFICATION USING A COMBINED ARTICULATORY PROSODY FRAMEWORK
Presented by: John Hansen, Author(s): Abhijeet Sangwan, Mahnoosh Mehrabani, John Hansen, The University of Texas at Dallas, United States
This study presents new advancements in our articulatory-based language identification (LID) system. Our LID system automatically identifies language-features (LFs) from a phonological features (PFs) based representation of speech. While our baseline system uses a static PF-representation for extracting LFs, the new system is based on a dynamic PF representation for feature extraction. Interestingly, the new LFs outperform our baseline system by 11.8% absolute in a difficult 5-way classification task of South Indian Languages. Additionally, we incorporate pitch and energy based features in our new system to leverage prosody in classification. In particular, we employ a Legendre polynomial based contour-estimation to capture shape parameters which are used in classification. Additionally, the fusion of PF and prosody-based LFs further improves the overall classification result by 16.5% absolute over the baseline system. Finally, the proposed articulatory language ID system is combined with a PPRLM (parallel phone recognition language model) system to obtain an overall classification accuracy of 86.6%.