estimateVTL: Estimate vocal tract length

Description

Estimates the length of vocal tract based on formant frequencies, assuming that the vocal tract can be modeled as a tube open at both ends.

Usage

estimateVTL(
  formants,
  method = c("meanFormant", "meanDispersion", "regression")[3],
  speedSound = 35400,
  checkFormat = TRUE
)

Arguments

formants

a character string like "aaui" referring to default presets for speaker "M1"; a vector of formant frequencies; or a list of formant times, frequencies, amplitudes, and bandwidths, with a single value of each for static or multiple values of each for moving formants

method

the method of estimating vocal tract length (see details)

speedSound

speed of sound in warm air, cm/s. Stevens (2000) "Acoustic phonetics", p. 138

checkFormat

if TRUE, expands shorthand format specifications into the canonical form of a list with four components: time, frequency, amplitude and bandwidth for each format (as returned by the internal function reformatFormants)

Value

Returns the estimated vocal tract length in cm.

Details

If method = 'meanFormant', vocal tract length (VTL) is calculated separately for each formant as \((2 * formant_number - 1) * speedSound / (4 * formant_frequency)\), and then the resulting VTLs are averaged. If method = 'meanDispersion', formant dispersion is calculated as the mean distance between formants, and then VTL is calculated as \(speed of sound / 2 / formant dispersion\). If method = 'regression', formant dispersion is estimated using the regression method described in Reby et al. (2005) "Red deer stags use formants as assessment cues during intrasexual agonistic interactions". For a review of these and other VTL-related summary measures of formant frequencies, refer to Pisanski et al. (2014) "Vocal indicators of body size in men and women: a meta-analysis". See also schwa for VTL estimation with additional information on formant frequencies.

Examples

Run this code

# NOT RUN {
estimateVTL(NA)
estimateVTL(500)
estimateVTL(c(600, 1850, 3100))
estimateVTL(formants = list(f1 = 600, f2 = 1650, f3 = 2400))

# Missing values are OK
estimateVTL(c(600, 1850, 3100, NA, 5000))

# For moving formants, frequencies are averaged over time,
# i.e. this is identical to c(600, 1650, 2400)
estimateVTL(formants = list(f1 = c(500, 700), f2 = 1650, f3 = c(2200, 2600)))

# Note that VTL estimates based on the commonly reported 'meanDispersion'
# depend only on the first and last formant
estimateVTL(c(500, 1400, 2800, 4100), method = 'meanDispersion')
estimateVTL(c(500, 1100, 2300, 4100), method = 'meanDispersion') # identical
# ...but
estimateVTL(c(500, 1400, 2800, 4100), method = 'meanFormant')
estimateVTL(c(500, 1100, 2300, 4100), method = 'meanFormant') # much longer

# }
# NOT RUN {
# Compare the results produced by the three methods
nIter = 1000
out = data.frame(meanFormant = rep(NA, nIter), meanDispersion = NA, regression = NA)
for (i in 1:nIter) {
  # generate a random formant configuration
  f = runif(1, 300, 900) + (1:6) * rnorm(6, 1000, 200)
  out$meanFormant[i]    = estimateVTL(f, method = 'meanFormant')
  out$meanDispersion[i] = estimateVTL(f, method = 'meanDispersion')
  out$regression[i]     = estimateVTL(f, method = 'regression')
}
pairs(out)
cor(out)
# 'meanDispersion' is pretty different, while 'meanFormant' and 'regression'
# give broadly comparable results
# }