This paper introduces a new set of cepstral features, based on a slightly modified version of the time-varying linear predictive models pioneered by Subba Rao and Liporace. In these models, the non-stationarity of the speech signal is accommodated by expressing the filter coefficients as a weighted combination of known basis functions. By running the parameterized filter coefficients through the recursive link between all-pole models and cepstral coefficients, we obtain a time-varying cepstral representation in analytical form. In a preliminary recognition experiment this representation is shown to give a satisfactory performance. It is argued that the introduced features are well suited for tasks such as detection of landmarks and stationary segments.
Cite as: Skogstad, T., Svendsen, T. (2008) Time-varying cepstral coefficients. Proc. ISCA ITRW on Speech Analysis and Processing for Knowledge Discovery, paper 053
@inproceedings{skogstad08_spkd,
author={Trond Skogstad and Torbjørn Svendsen},
title={{Time-varying cepstral coefficients}},
year=2008,
booktitle={Proc. ISCA ITRW on Speech Analysis and Processing for Knowledge Discovery},
pages={paper 053}
}