Pitch Strength as an Outcome Measure for Treatment of Dysphonia

Abstract

Background Measurement of treatment outcomes is critical for of the spectrum of voice treatments, (i.e., surgical, behavioral, or pharmacological). Outcome measures typically include visual (e.g., stroboscopic data), auditory (e.g., CAPE-V, GRBAS), and objective correlates of vocal fold vibratory characteristics, such as acoustic signals (e.g., harmonics-to-noise ratio, cepstral peak prominence) or patient self-reported questionnaires (e.g., Voice Handicap Index, Voice-Related Quality of Life). Subjective measures often show high variability, while most acoustic measures of voice are only valid for signals where some degree of periodicity can be assumed. However, this assumption is often invalid for dysphonic voices where signal periodicity is suspect. Furthermore, many of these measures are not useful in isolation for diagnostic purposes. Objective We evaluated a recently developed algorithm (Auditory-SWIPE’) for estimating pitch and pitch strength for dysphonic voices. While fundamental frequency is a physical attribute of a signal, pitch is its psychophysical correlate. As such, the perception of pitch can extend to most signals irrespective of their periodicity. Methods Post-hoc analyses were conducted for three groups of patients evaluated and treated for voice problems at a major voice center: (1) muscle tension dysphonia/functional dysphonia, (2) vocal fold mass(es), and (3) presbyphonia. All patients were recorded before and after surgical/behavioral treatment for voice disorders. Pitch and pitch strength for each speaker were computed with the Auditory-SWIPE’ algorithm. Results Comparison of pre-and post-treatment data provides support for pitch strength as a measure of treatment outcomes for dysphonic voices.

Voice deviation, dysphonia risk screening and quality of life in individuals with various laryngeal diagnoses

“…The results of our APA concur with previous studies; we observed a wide variability in the degree of voice deviation that was not related to the etiology of dysphonia 1 , 2 , 17 .…”

“…According to reports in the scientific literature, in addition to the anatomical and physiological issues that predispose women to voice disorders 21 , women seek medical care more often than men 23 , 24 ; furthermore, some voice disorders are more prevalent in female patients 25 . Kopf et al 17 analyzed 30 cases of dysphonia and found that the most common diagnoses in women were muscle tension, functional disorders, and mass lesions of the vocal folds. Other studies also found a greater prevalence of dysphonia in adult women 21 , 23 .…”

“…In AA, various quantitative and qualitative data are collected using computer software 17 . AA assumes that vocal acoustic signals validate auditory-perceptual signals and vice versa because AA allows the physiological and auditory-perceptual spheres to be integrated 18 .…”

“…For example, in the presence of laryngeal disorders, abnormal jitter is correlated with fundamental frequency measurements; furthermore, there is a correlation between abnormal shimmer measurements and disorders that interfere with voice intensity and between roughness and mass lesions in the vocal folds 19 . However, AA may not be reliable in cases of severe vocal abnormalities given the limitations of currently available software; in addition, many acoustic measurements have no diagnostic value when they are considered separately 17 .…”

Sensitivity analysis of muscle mechanics-based voice simulator to determine gender-specific speech characteristics

“…In an attempt to capture the quality of a vibration, pitch strength was also estimated (Camacho and Harris 2008). Pitch strength is a psychoacoustic construct of the pitch saliency of a sound (the degree that a tone can be heard in a sound) which is similar to a periodicity measure and correlates to perceptual voice quality (Kopf et al 2017). Fundamental frequency (f 0 ) and pitch strength were calculated from the glottal flow waveform for each signal using the Aud-SWIPE pitch estimation algorithm (Camacho 2012) in MATLAB.…”