A nonlinear operator-based speech feature analysis method with application to vocal fold pathology assessment

Traditional speech processing methods for laryngeal pathology assessment assume linear speech production with measures derived from an estimated glottal flow waveform. They normally require the speaker to achieve complete glottal closure, which for many vocal fold pathologies cannot be accomplished. To address this issue, a nonlinear signal processing approach is proposed which does not require direct glottal flow waveform estimation. This technique is motivated by earlier studies of airflow characterization for human speech production. The proposed nonlinear approach employs a differential Teager energy operator and the energy separation algorithm to obtain formant AM and FM modulations from filtered speech recordings. A new speech measure is proposed based on parameterization of the autocorrelation envelope of the AM response. This approach is shown to achieve impressive detection performance for a set of muscular tension dysphonias. Unlike flow characterization using numerical solutions of Navier-Stokes equations, this method is extremely computationally attractive, requiring only a small time window of speech samples. The new noninvasive method shows that a fast, effective digital speech processing technique can be developed for vocal fold pathology assessment without the need for direct glottal flow estimation or complete glottal closure by the speaker. The proposed method also confirms that alternative nonlinear methods can begin to address the limitations of previous linear approaches for speech pathology assessment.