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• W2119347058 abstract "In this paper, we focus on methods for estimating the a posteriori probability of a signal segment being voiced which employ a harmonic signal model. Fisher et al. [1] present two likelihood functions for voiced and unvoiced speech from which the posterior probability can be derived. However, due to the chosen models, the a posteriori probability of a signal segment being voiced does not go to 0 % in unvoiced speech. Thus, a novel algorithm is proposed, which incorporates the expected unvoiced speech energy and allows for obtaining low probabilities. Further, it explicitly models the statistics of the segment energy and employs a state-of-the-art noise tracker. Experiments which were conducted on the TIMIT database for different noise types and noise levels show that the proposed method results in lower over-estimation and under-estimation of the voicing probability as compared to [1]." @default.
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• W2119347058 date "2014-05-01" @default.
• W2119347058 modified "2023-09-23" @default.
• W2119347058 title "A posteriori voiced/unvoiced probability estimation based on a sinusoidal model" @default.
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• W2119347058 doi "https://doi.org/10.1109/icassp.2014.6854946" @default.
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