Vocal fold pathology assessment using AM autocorrelation analysis of the teager energy operator

Traditional speech processing methods for laryngeal pathology assessment assume linear speech production, with measures derived from an estimated glottal ow waveform. They normally require the speaker to achieve complete glottal closure, which for many vocal fold pathologies cannot be accomplished. To address this, a nonlinear signal processing approach is proposed which employs a di erential Teager energy operator and the energy separation algorithm to obtain formant AM and FM modulations from bandpass ltered speech recordings. A new speech measure is proposed based on parameterization of the autocorrelation envelop of the AM response. Using a cubic model of the autocorrelation envelop, a three dimensional space is formed to assess changes in speech quality. This approach is shown to achieve exemplary detection performance for a set of muscular tension dysphonias. Unlike ow characterization using numerical solutions of Navier Stokes equations, this method is extremely computationally attractive, requiring only N logN+8N multiplications and N square roots for N samples, and is therefore suitable for real time applications due to its computational simplicity. The new non-invasive method shows conclusively that a fast, e ective digital speech processing technique can be developed for vocal fold pathology assessment, without the need for (i) direct glottal ow estimation or (ii) complete glottal closure by the speaker. The proposed method also con rms that alternative nonlinear methods can begin to address the limitations of previous linear approaches for speech pathology assessment.