ABSTRACT
OBJECTIVES: Relative fundamental frequency (RFF) has shown promise as an acoustic measure of voice, but the subjective and time-consuming nature of its manual estimation has made clinical translation infeasible. Here, a faster, more objective algorithm for RFF estimation is evaluated in a large and diverse sample of individuals with and without voice disorders. METHODS: Acoustic recordings were collected from 154 individuals with voice disorders and 36 age- and sex-matched controls with typical voices. These recordings were split into training and 2 testing sets. Using an algorithm tuned to the training set, semi-automated RFF estimates in the testing sets were compared to manual RFF estimates derived from 3 trained technicians. RESULTS: The semi-automated RFF estimations were highly correlated ( r = 0.82-0.91) with the manual RFF estimates. CONCLUSIONS: Fast and more objective estimation of RFF makes large-scale RFF analysis feasible. This algorithm allows for future work to optimize RFF measures and expand their potential for clinical voice assessment.
Subject(s)
Algorithms , Voice/physiology , Adolescent , Adult , Aged , Aged, 80 and over , Case-Control Studies , Female , Humans , Male , Middle Aged , Speech Acoustics , Voice Disorders/diagnosis , Young AdultABSTRACT
OBJECTIVE: High voice users (individuals who demonstrate excessive or loud vocal use) are at risk for developing voice disorders. The objective of this study was to examine, both acoustically and perceptually, vocal changes in healthy speakers after an acute period of high voice use. METHODS: Members of a university women's volleyball team (n = 12) were recorded a week before (pre) and week after (post) the 10-week spring season; n = 6 control speakers were recorded over the same time period for comparison. Speakers read four sentences, which were analyzed for relative fundamental frequency (RFF). Eight naïve listeners participated in an auditory-perceptual visual sort and rate (VSR) task, in which they rated each voice sample's overall severity and strain. RESULTS: No significant differences were found as a function of time point in the VSR ratings for the volleyball group. Onset cycle 1 RFF values were significantly lower (P = 0.04) in the postrecordings of the volleyball participants compared with prerecordings, but there was no significant difference (P = 0.20) in offset cycle 10 RFF values. Receiver operating characteristic analyses indicated moderate sensitivity and specificity of onset cycle 1 RFF for discrimination between the volleyball and control participants. Changes were not apparent in the control group as a function of time for either, onset cycle 1 RFF, offset cycle 10 RFF, or either vocal attribute. CONCLUSIONS: Onset cycle 1 RFF may be an effective marker for detecting vocal changes over an acute high voice use period of time before perceptual changes are noted.
Subject(s)
Athletes , Dysphonia/psychology , Speech Acoustics , Speech Perception , Voice Quality , Volleyball , Acoustics , Adolescent , Adult , Case-Control Studies , Dysphonia/diagnosis , Dysphonia/physiopathology , Female , Humans , Judgment , Severity of Illness Index , Sound Spectrography , Speech Production Measurement , Young AdultABSTRACT
PURPOSE: This study investigated the use of neck-skin acceleration for relative fundamental frequency (RFF) analysis. METHOD: Forty individuals with voice disorders associated with vocal hyperfunction and 20 age- and sex-matched control participants were recorded with a subglottal neck-surface accelerometer and a microphone while producing speech stimuli appropriate for RFF. Rater reliabilities, RFF means, and RFF standard deviations derived from the accelerometer were compared with those derived from the microphone. RESULTS: RFF estimated from the accelerometer had slightly higher intrarater reliability and identical interrater reliability compared with values estimated with the microphone. Although sensor type and the Vocal Cycle × Sensor and Vocal Cycle × Sensor × Group interactions showed significant effects on RFF means, the typical RFF pattern could be derived from either sensor. For both sensors, the RFF of individuals with vocal hyperfunction was lower than that of the controls. Sensor type and its interactions did not have significant effects on RFF standard deviations. CONCLUSIONS: RFF can be reliably estimated using an accelerometer, but these values cannot be compared with those collected via microphone. Future studies are needed to determine the physiological basis of RFF and examine the effect of sensors on RFF in practical voice assessment and monitoring settings.
