Multi-stream dialect classification using SVM-GMM hybrid classifiers

In this paper, we investigate two important issues that influence dialect classification: (i) exploring dialect dependent features, and (ii) an effective way of combining spectral, excitation, and vocal tract information to improve dialect classification. The motivation is that dialect dependent features such as formants, LSP (line spectral pairs) and MEPZ (MFCCs + energy + pitch) span a wider range of speech production traits and are therefore better suited than traditional MFCCs for characterizing dialects. After establishing the proposed algorithm, we compare individual performances of each feature on a corpus of three dialects of Spanish. Next, we present a method for combining these features using GMM-SVM hybrid classifiers. The final combined system achieves a 30% relative improvement in dialect classification accuracy, confirming that the proposed advances significantly outperform conventional methods for dialect classification.