Image representation of the acoustic signal: An effective tool for modeling spectral and temporal dynamics of connected speech

Abstract

Recent studies have advocated for the use of connected speech in clinical voice and speech assessment. This suggestion is based on the presence of clinically relevant information within the onset, offset, and variation in connected speech. Existing works on connected speech utilize methods originally designed for analysis of sustained vowels and, hence, cannot properly quantify the transient behavior of connected speech. This study presents a non-parametric approach to analysis based on a two-dimensional, temporal-spectral representation of speech. Variations along horizontal and vertical axes corresponding to the temporal and spectral dynamics of speech were quantified using two statistical models. The first, a spectral model, was defined as the probability of changes between the energy of two consecutive frequency sub-bands at a fixed time segment. The second, a temporal model, was defined as the probability of changes in the energy of a sub-band between consecutive time segments. As the first step of demonstrating the efficacy and utility of the proposed method, a diagnostic framework was adopted in this study. Data obtained revealed that the proposed method has (at minimum) significant discriminatory power over the existing alternative approaches.