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It has long been claimed that spectral envelope features outperform prosodic features on speaker recognition tasks. However, the reasons for such an arrangement are not entirely compelling. In the current work we present some evidence to challenge these claims. We propose that energy found at harmonically related frequencies encodes the acoustic correlates of variables which are typically referred to as prosodic, making harmonic energy summation highly relevant. Its frequent implementation for estimating pitch appears to have gone unnoticed by the speaker recognition community, because pitch estimators quite deliberately discard what they compute, retaining only the abscissa of a maximum. We argue that this latter step renders pitch estimation somewhat ill-suited to speaker recognition tasks. We present the detailed construction of a discrete transform, and a normalization, which are amenable to relatively laconic modeling. With this framework we achieve or exceed the performance of spectral envelope features in nearfield, matched-channel and matched multisession conditions; performance improves following envelope destruction. We believe these results may have far-reaching consequences. For speech processing in a multitude of applications, they suggest that modeling the harmonic structure in the way we propose is at least as relevant as is modeling other aspects of the signal.

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