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- W3107053357 abstract "Speaker specific information present in the excitation signal is mostly viewed from sub-segmental, segmental and supra-segmental levels. In this work, the supra-segmental level information is explored for recognizing speakers. Earlier study has shown that, combined use of pitch and epoch strength vectors provides useful supra-segmental information. However, the speaker recognition accuracy achieved by supra-segmental level feature is relatively poor than other levels source information. May be the modulation information present at the supra-segmental level of the excitation signal is not manifested properly in pith and epoch strength vectors. We propose a method to model the supra-segmental level modulation information from residual mel frequency cepstral coefficient (R-MFCC) trajectories. The evidences from R-MFCC trajectories combined with pitch and epoch strength vectors are proposed to represent supra-segmental information. Experimental results show that compared to pitch and epoch strength vectors, the proposed approach provides relatively improved performance. Further, the proposed supra-segmental level information is relatively more complimentary to other levels information." @default.
- W3107053357 created "2020-12-07" @default.
- W3107053357 creator A5052129812 @default.
- W3107053357 creator A5060241072 @default.
- W3107053357 date "2013-01-01" @default.
- W3107053357 modified "2023-09-30" @default.
- W3107053357 title "Speaker Recognition using Supra-segmental Level Excitation Information" @default.
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- W3107053357 doi "https://doi.org/10.47893/ijcct.2013.1165" @default.
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