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- W2395928727 abstract "This paper presents speaker normalization approaches for audio search task. Conventional state-of-the-art feature set, viz., Mel Frequency Cepstral Coefficients (MFCC) is known to contain speaker-specific and linguistic information implicitly. This might create problem for speaker-independent audio search task. In this paper, universal warping-based approach is used for vocal tract length normalization in audio search. In particular, features such as scale transform and warped linear prediction are used to compensate speaker variability in audio matching. The advantage of these features over conventional feature set is that they apply universal frequency warping for both the templates to be matched during audio search. The performance of Scale Transform Cepstral Coefficients (STCC) and Warped Linear Prediction Cepstral Coefficients (WLPCC) are about 3% higher than the state-of-the-art MFCC feature sets on TIMIT database." @default.
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- W2395928727 date "2015-01-01" @default.
- W2395928727 modified "2023-10-16" @default.
- W2395928727 title "Vocal Tract Length Normalization Features for Audio Search" @default.
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- W2395928727 doi "https://doi.org/10.1007/978-3-319-24033-6_44" @default.
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