| Autor: |
Santosh Kesiraju, Sudarsana Reddy Kadiri, Rashmi Kethireddy, Suryakanth V. Gangashetty |
| Přispěvatelé: |
International Institute of Information Technology Hyderabad, Dept Signal Process and Acoust, Brno University of Technology, Aalto-yliopisto, Aalto University |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Odyssey |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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