Perceptually based processing in automatic speech recognition

The perceptually based linear predictive (PLP) speech analysis method is applied to isolated word automatic speech recognition (ASR). Low dimensionality of the PLP analysis vector, which is otherwise identical in form to the standard linear predictive (LP) analysis vector, allows for computational and storage savings in ASR. We show that in speaker-dependent recognition of the alpha-numeric vocabulary, the PLP method in VQ-based ASR yields similar recognition scores as does the standard ASR system. The main focus of the paper is on cross-speaker ASR. We demonstrate in experiments with vowel centroids of two male and one female speakers that PLP speech representation is more consistent with the underlying phonetic information than the standard LP method. Conclusions from the experiments are confirmed by superior performance of the PLP method in cross-speaker isolated word recognition.