title: An Objective Parameter for Quantifying the Turbulent Noise Portion of Voice Signals.
abstract: OBJECTIVE
  Currently, there are no objective measures capable of distinguishing between all four voice signal types proposed by Titze in 1995 and updated by Sprecher in 2010. We propose an objective metric that distinguishes between voice signal types based on the aperiodicity present in a signal.
  
  
  METHODS
  One hundred fifty voice signal samples were randomly selected from the Disordered Voice Database and subjectively sorted into the appropriate voice signal category on the basis of the classification scheme presented in Sprecher 2010.
  
  
  METHODS
  Short-time Fourier transform was applied to each voice sample to produce a spectrum for each signal. The spectrum of each signal was divided into 250 time segments. Next, these segments were compared to each other and used to calculate an outcome named spectrum convergence ratio (SCR). Finally, the mean SCR was calculated for each of the four voice signal types.
  
  
  RESULTS
  SCR was capable of significantly differentiating between each of the four voice signal types (P < 0.001). Additionally, this new parameter proved equally as effective at distinguishing between voice signal types as currently available parameters.
  
  
  CONCLUSIONS
  SCR was capable of objectively distinguishing between all four voice signal types. This metric could be used by clinicians to quickly and efficiently diagnose voice disorders and monitor improvements in voice acoustical signals during treatment methods.
authors.affiliation: Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: Liyu
authors.initials: L
authors.lastname: Lin
authors.affiliation: Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: William
authors.initials: W
authors.lastname: Calawerts
authors.affiliation: Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin.
authors.forename: Keith
authors.initials: K
authors.lastname: Dodd
authors.affiliation: Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin. Electronic address: jjjiang@wisc.edu.
authors.forename: Jack J
authors.initials: JJ
authors.lastname: Jiang
mesh_terms: D000162:Acoustics; D000704:Analysis of Variance; D016208:Databases, Factual; D005583:Fourier Analysis; D006801:Humans; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D013997:Time Factors; D014832:Voice Disorders / Q000175:diagnosis* / Q000503:physiopathology / Q000628:therapy; D014833:Voice Quality
keywords: Short time Fourier transform; Signal spectrum analysis; Spectrum convergence ratio; Turbulence; Voice signal classification