The pitch-scaled harmonic filter (PSHF) is a technique for decomposing speech signals into their voiced and unvoiced constituents. In this paper, we evaluate its ability to reconstruct the time series of the two components accurately using a variety of synthetic, speech-like signals, and discuss its performance. These results determine the degree of confidence that can be expected for real speech signals: typically, 5 dB improvement in the signal-to-noise ratio (HNR) in the anharmonic component. A selection of the analysis oportunities that the decomposition offers is demonstrated on speech recording, including dynamic HNR estimation and separate linear prediction analyses of the two components. These new capabilities provided by the PSHF can facilitate discovering previously hidden features and investigating interactions of unvoiced sources, such as friction, with voicing.
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