Perceptual linear predictive (PLP) analysis of speech

TLDR

The paper introduces and evaluates the perceptual linear predictive (PLP) technique for speech analysis. PLP derives the auditory spectrum using critical‑band resolution, equal‑loudness weighting, and the intensity‑loudness power law, then models it with an autoregressive all‑pole filter. A 5th‑order all‑pole PLP model suppresses speaker‑dependent details, aligns better with human hearing, captures vowel‑perception theories, is computationally efficient, and improves speaker‑independent automatic speech recognition.

Abstract

A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, is presented and examined. This technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal-loudness curve, and (3) the intensity-loudness power law. The auditory spectrum is then approximated by an autoregressive all-pole model. A 5th-order all-pole model is effective in suppressing speaker-dependent details of the auditory spectrum. In comparison with conventional linear predictive (LP) analysis, PLP analysis is more consistent with human hearing. The effective second formant F2' and the 3.5-Bark spectral-peak integration theories of vowel perception are well accounted for. PLP analysis is computationally efficient and yields a low-dimensional representation of speech. These properties are found to be useful in speaker-independent automatic-speech recognition.