Active Ingredients of Voice Therapy for Muscle Tension Voice Disorders: A Retrospective Data Audit

Connecting Auditory-Perceptual Prompts Used in Voice Therapy to Anatomy and Physiology: Application to the Estill Voice Model and the Rehabilitation Treatment Specification System

PURPOSE

This clinical tutorial will present the concept of applying auditory-perceptual prompts (implicit instruction) typically used in voice therapy to the anatomy and physiology of the voice production system (explicit instruction) via the Estill Voice Model (EVM) and the Rehabilitation Treatment Specification System (RTSS).

METHODS

EVM offers an integrated implicit-explicit instructional approach to voice training allowing for isolated practice of vocal structures (explicit) that interact to produce functional voice qualities (implicit), such as modal speech and louder projected voice qualities. In EVM, voice quality is correlated with the specific anatomy and physiologic adjustments via 13 Estill Figures and Options (eg, Larynx Figure has three options: High, Mid, and Low). RTSS provides a framework to connect client change in functioning (ie, target) with clinician action (ie, ingredients). Mechanisms of action connect the target to the ingredients by hypothesizing how the treatment is expected to work.

RESULTS

Evidence is provided for connecting auditory-perpetual voice prompts with the anatomy and physiology of voice and supporting an integrated implicit-explicit approach to voice therapy. The concept of linking commonly used implicit auditory-perceptual prompts used in voice therapy (eg, humming, loud "aahh") to explicit anatomy and physiology training (eg, 13 Estill Figures and Options) is demonstrated using EVM and the RTSS framework with case studies and video examples.

CONCLUSIONS

Clinicians may choose to use anatomy and physiology of voice to define and provide explicit instruction for typically used implicit auditory-perceptual prompts. Future research is warranted to test the concept applied to voice therapy models in the literature across prevention and treatment of voice disorders.

Auditory-perceptual Parameters as Predictors of Voice Acoustic Measures

BACKGROUND

Much research has examined the relationship between perceptual and acoustic measures. However, little is known about the prediction values of perceptual measures on an acoustic parameter.

AIMS

This study utilized simulated and disordered voice samples to investigate the prediction values of breathiness, roughness, and strain ratings on the selection of some time-based and spectral-based measures of voice quality.

METHOD

This study retrospectively analysed two sets of precollected data. The experimental data had been collected from nine trained speakers manipulating false vocal fold activity, true vocal fold mass, and larynx height. The voice-disordered data had been extracted from a clinical database for 68 patients with muscle tension voice disorders (MTVD). Both data sets had been perceptually rated for breathiness, roughness, and strain. Voice samples (prolonged vowel /ɑ/ and Rainbow Passage readings) had undergone acoustic analysis using Praat for harmonics-to-noise ratio (HNR) and the program "Analysis of Dysphonia in Speech and Voice" (ADSV) for cepstral peak prominence (CPP), Cepstral/Spectral Index of Dysphonia (CSID), and Low/High spectral ratio (L/H ratio). Perceptual parameters were regressed against these acoustic measures to test their prediction values.

RESULTS

Reliability data showed satisfactory intra- and inter-reliability of perceptual ratings for both data sets. Breathiness significantly predicted CPP (both vocal tasks) and CSID (Rainbow Passage) in experimental data and predicted all the acoustic measures in MTVD data. Roughness significantly predicted HNR, CPP, and CSID in experimental data, and CPP (Rainbow Passage) and CSID (both vocal tasks) in MTVD data. Strain (both vocal tasks) significantly predicted L/H ratio in both data sets.

CONCLUSIONS

Breathiness ratings predicted selection of HNR, CPP and CSID; roughness ratings predicted selection of CPP and CSID, and strain ratings predicted L/H ratio.