title: Modeling of Breathy Voice Quality Using Pitch-strength Estimates.
abstract: BACKGROUND
  The characteristic voice quality of a speaker conveys important linguistic, paralinguistic, and vocal health-related information. Pitch strength refers to the salience of pitch sensation in a sound and was recently reported to be strongly correlated with the magnitude of perceived breathiness based on a small number of voice stimuli.
  
  
  OBJECTIVE
  The current study examined the relationship between perceptual judgments of breathiness and computational estimates of pitch strength based on the Aud-SWIPE (P-NP) algorithm for a large number of voice stimuli (330 synthetic and 57 natural).
  
  
  RESULTS
  Similar to the earlier study, the current results confirm a strong relationship between estimated pitch strength and listener judgments of breathiness such that low pitch-strength values are associated with voices that have high perceived breathiness. Based on this result, a model was developed for the perception of breathy voice quality using a pitch-strength estimator. Regression functions derived between the pitch-strength estimates and perceptual judgments of breathiness obtained from matching task revealed a linear relationship for a subset of the natural stimuli. We then used this function to obtain predicted breathiness values for the synthetic and the remaining natural stimuli.
  
  
  CONCLUSIONS
  Predicted breathiness values from our model were highly correlated with the perceptual data for both types of stimuli. Systematic differences between the breathiness of natural and synthetic stimuli are discussed.
authors.affiliation: Department of Communication Sciences and Disorders, University of South Florida, Tampa, Florida. Electronic address: deddins@usf.edu.
authors.forename: David A
authors.initials: DA
authors.lastname: Eddins
authors.affiliation: Department of Communication Sciences and Disorders, West Chester University, West Chester, Pennsylvania.
authors.forename: Supraja
authors.initials: S
authors.lastname: Anand
authors.affiliation: School of Computer Science and Informatics, University of Costa Rica, San Jose, Costa Rica.
authors.forename: Arturo
authors.initials: A
authors.lastname: Camacho
authors.affiliation: Office of the Vice President for Instruction, University of Georgia, Athens, Georgia.
authors.forename: Rahul
authors.initials: R
authors.lastname: Shrivastav
mesh_terms: D000161:Acoustic Stimulation; D000162:Acoustics; D000328:Adult; D000465:Algorithms; D055154:Dysphonia / Q000175:diagnosis* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007600:Judgment; D016014:Linear Models; D008297:Male; D010699:Phonation; D010898:Pitch Perception; D011237:Predictive Value of Tests; D011571:Psychoacoustics; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D014833:Voice Quality; D055815:Young Adult
keywords: Aud-SWIPE (P-NP); Breathiness; Listener perception; Matching task; Pitch strength