Spectral features are highly sensitive to acoustic mismatches, including variations in pitch, speaking rate, and ambient noise. To address these challenges, this paper presents a method for parameterizing subband speech signals in the temporal domain, resulting in a pitch-robust acoustic feature representation. The method involves decomposing the speech signal into subband complementary signals using \(N\) equal-bandwidth band-pass filters. The short-term temporal slope of each band-pass signal is calculated by applying a first-order infinite impulse response (IIR) low-pass filter, followed by a non-local differencing operation. These slope values are then logarithmically compressed to generate an \(N\) -dimensional feature vector for each analysis frame. The resulting feature, termed the logarithmic compressed subband temporal slope (LC-SBTS), primarily captures the energy transition patterns of sound units while suppressing speaker-specific traits. The robustness of the proposed features is demonstrated through analytical validation using t-distributed stochastic neighbor embedding, evaluation of keyword-spotting performance under pitch-mismatched test conditions, and comparison with existing pitch-normalized feature computation techniques.
