This paper presents a set of cepstral parameters based on timevarying linear prediction. The lattice filter structure is utilized to accommodate efficient stabilization of models and a Bark-like warped frequency scale. As the proposed cepstral features are based on non-stationary spectral analysis there is a potential for complementary information not captured in conventional features. In classification and recognition experiments, the proposed features are shown to improve performance when augmenting MFCCs.