Chapter 9 - Temporal and Spatial Features of Speech Signals

Abstract

Autocorrelation-based features as a basis for phonetic and syllabic distinctions are discussed. The work springs from a theory of auditory signal processing based on central monaural autocorrelation and binaural cross-correlation representations. Correlation-based features are used to predict monaural and binaural perceptual attributes that are important for the architectural acoustic design of concert halls, as well as temporal primary percepts, that is, pitch, timbre, loudness, duration, and spatial primary percepts, such as sound direction, apparent source width, and envelopment. This chapter investigates the use of features of monaural autocorrelation functions (ACFs) for representing phonetic elements (vowels), syllables (consonant-vowel [CV]), and phrases using a small set of temporal factors extracted from the short-term running ACF. These factors include listening level (LL, loudness), Wϕ(0), zero-lag ACF peak width (spectral tilt), τ1 (voice pitch period), ϕ1 (voice pitch strength), τe (effective duration of the ACF envelope, temporal repetitive continuity/contrast), segment duration, and Δϕ1/Δt (the rate of pitch strength change, related to voice pitch attack-decay dynamics). Times at which ACF effective duration, τe, is minimal reflect rapid signal pattern changes that usefully demarcate segmental boundaries. Results suggest that vowels, CV syllables, and phrases can be distinguished on the basis of this ACF-derived feature set. In addition, effects of spatial factors extracted from the interaural cross-correlation function (IACF) adding to ACF temporal factors are discussed.