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- W2103202874 abstract "In this letter, we present a statistical approach to Mel-domain mask estimation for missing feature (MF)-based automatic speech recognition (ASR). Mel-domain time-frequency masks are of interest, since MF systems have been shown successful in that domain. Time- and channel-specific reliability measures are derived as posterior probabilities of active speech using a 2-state speech model. Since closed form distributions for Mel-domain spectra do not exist, they are instead modeled as χ <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> processes with empirically-determined degrees of freedom. Additionally, we present HMM-based decoding to exploit temporal correlation of spectral speech data. The proposed mask estimation algorithm is integrated with an example MF-based ASR front-end from, and is shown to outperform the spectral subtraction (SS)-based method from in terms of word-accuracy, when applied to the Aurora-2 database." @default.
- W2103202874 created "2016-06-24" @default.
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- W2103202874 date "2010-11-01" @default.
- W2103202874 modified "2023-10-17" @default.
- W2103202874 title "A Statistical Approach to Mel-Domain Mask Estimation for Missing-Feature ASR" @default.
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- W2103202874 doi "https://doi.org/10.1109/lsp.2010.2076348" @default.
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