Dialect Classification for Online Podcasts Fusing Acoustic and Language Based Structural and Semantic Information

The variation in speech due to dialect is a factor which significantly impacts speech system performance. In this study, we investigate effective methods of combining acoustic and language information to take advantage of (i) speaker based acoustic traits as well as (ii) content based word selection across the text sequence. For acoustics, a GMM based system is employed and for text based dialect classification, we proposed n-gram language models combined with Latent Semantic Analysis (LSA) based dialect classifiers. The performance of the individual classifiers is established for the three dialect family case (DC rates vary from 69.1%-72.4%). The final combined system achieved a DC accuracy of 79.5% and significantly outperforms the baseline acoustic classifier with a relative improvement of 30%, confirming that an integrated dialect classification system is effective for American, British and Australian dialects.