Zero-Time Windowing Cepstral Coefficients for Dialect Classification
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A4 Artikkeli konferenssijulkaisussa
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en
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7
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The Speaker and Language Recognition Workshop (Odyssey) 2020 , pp. 32-38, Odyssey : the Speaker and Language Recognition Workshop
Abstract
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.Description
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Kethireddy, R, Kadiri, S, Kesiraju, S & Gangashetty, S 2020, Zero-Time Windowing Cepstral Coefficients for Dialect Classification. in The Speaker and Language Recognition Workshop (Odyssey) 2020 . Odyssey : the Speaker and Language Recognition Workshop, International Speech Communication Association (ISCA), pp. 32-38, Odyssey: The Speaker and Language Recognition Workshop, Tokyo, Japan, 01/11/2021. https://doi.org/10.21437/Odyssey.2020-5