Direct Speech Feature Estimation Using an Iterative EM Algorithm for Vocal Fold Pathology Detection

The focus of this study is to formulate a speech parameter estimation algorithm for analysis/detection of vocal fold pathology. The speech processing algorithm proposed estimates features necessary to formulate a stochastic model to characterize healthy and pathology conditions, from speech recordings. The general idea is to separate speech components under healthy and assumed pathology conditions. This problem is addressed using an iterative maximum likelihood (ML) estimation procedure, based on the Estimation-Maximization (EM) algorithm. A new feature for characterizing pathology, termed Enhanced Spectral Pathology Component (ESPC) is estimated and shown to vary consistently between healthy and pathology conditions. It is also shown that the Mean Area Peak Value (MAPV) and the weighted slope (WSLOPE) indexes, which are obtained from the ESPC estimate, are meaningful measures of speech pathology conditions. For classification purposes, a 5-state Hidden Markov Model (HMM) recognizer w..