In this paper, we propose to use novel acoustic features, namely zero-time windowing cepstral coefficients (ZTWCC) for dialect classification. ZTWCC features are derived from high-resolution spectrum obtained with zero-time windowing (ZTW) method, and were shown to be useful for discriminating speech sound characteristics effectively as compared to a DFT spectrum. Our proposed system is based on i-vectors trained on static and shifted delta coefficients of ZTWCC. The i-vectors are further whitened before classification. The proposed system is compared with i-vector baseline system trained on Mel frequency cepstral coefficient (MFCC) features. Classification results on STYRIALECT database (German) and UT-Podcast (English) database revealed that the system with proposed features outperformed aforementioned baseline system. Our detailed experimental analysis on dialect classification shows that the i-vector system can indeed exploit high spectral resolution of ZTWCC and hence performed better than MFCC features based system.
Cite as: Kethireddy, R., Kadiri, S.R., Kesiraju, S., Gangashetty, S.V. (2020) Zero-Time Windowing Cepstral Coefficients for Dialect Classification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2020), 32-38, doi: 10.21437/Odyssey.2020-5
@inproceedings{kethireddy20_odyssey,
author={Rashmi Kethireddy and Sudarsana Reddy Kadiri and Santosh Kesiraju and Suryakanth V. Gangashetty},
title={{Zero-Time Windowing Cepstral Coefficients for Dialect Classification}},
year=2020,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2020)},
pages={32--38},
doi={10.21437/Odyssey.2020-5}
}