Temporal patterns of frequency-localized features in ASR. This work investigates the use of frequency-localized temporal patterns of the speech signal for developing robust front-end for Automatic Speech Recognition (ASR). Various linear transforms are investigated for parameterization of the frequency-localized temporal patterns. We show that temporal patterns closely follow the properties of a first- order Markov process, which results in the PCA transforms being very close to the DCT transform. Better recognition performance is achieved on using the DCT components of temporal patterns as opposed to directly using temporal patterns for feature estimation. Other linear transforms such as Linear Discriminant Analysis (LDA) are also studied by the parameterization. The parameterized TempoRAl Patterns (TRAPS) are used to estimate broad-phonetic class-posteriors independently in each critical-band. These class- posteriors are combined and used as the features for word recognition. Our work shows that broad-phonetic features generalize better than other conventional features and yield considerable complementary information concerning short-term central features in ASR.