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- W1569242782 abstract "RESUMEN Currently, many speaker recognition applications must handle speech corrupted by environmental additiv e noise without having a priori knowledge about the charact eristics of noise. Some previous works in speaker r ecognition have used Missing Feature (MF) approach to compensate for noise. In most of those applications the spe ctral reliability decision step is done using the Signal to Noise Ratio (SNR) criterion. This has the goal o f enhancing signal power rather than noise power, which could b e dangerous in speaker recognition tasks, because u seful speaker information could be removed. This work proposes a new mask estimation method based on Speaker Discriminative Information (SDI) for determining spectral reliabil ity in speaker recognition applications based on th e MF approach. The proposal was evaluated through speaker verification experiments in speech corrupted by additive no ise. Experiments demonstrated that this new criterion ha s a promising performance in speaker verification t asks." @default.
- W1569242782 created "2016-06-24" @default.
- W1569242782 creator A5050011052 @default.
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- W1569242782 date "2012-06-01" @default.
- W1569242782 modified "2023-09-24" @default.
- W1569242782 title "New Missing Features Mask Estimation Method for Speaker Recognition in Noisy Environments" @default.
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- W1569242782 doi "https://doi.org/10.1234/rielac.v33i2.122" @default.
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