soundgen (version 2.7.2)

estimateVTL: Estimate vocal tract length

Description

Estimates the length of vocal tract based on formant frequencies. If method = 'meanFormant', vocal tract length (VTL) is calculated separately for each formant, and then the resulting VTLs are averaged. The equation used is \((2 * formant_number - 1) * speedSound / (4 * formant_frequency)\) for a closed-open tube (mouth open) and \(formant_number * speedSound / (2 * formant_frequency)\) for an open-open or closed-closed tube (eg closed mouth in mmm or open mouth and open glottis in whispering). 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.

Usage

estimateVTL(
  formants,
  method = c("regression", "meanDispersion", "meanFormant")[1],
  interceptZero = TRUE,
  tube = c("closed-open", "open-open")[1],
  speedSound = 35400,
  checkFormat = TRUE,
  output = c("simple", "detailed")[1],
  plot = FALSE
)

Value

If output = 'simple' (default), returns the estimated vocal tract length in cm. If output = 'detailed' and method = 'regression', returns a list with extra stats used for plotting. Namely,

$regressionInfo$infl gives the influence of each observation calculated as the absolute change in VTL with vs without the observation * 10 + 1 (the size of labels on the first plot). $vtlPerFormant$vtl

gives the VTL as it would be estimated if only the first nFormants

were used.

Arguments

formants

formant frequencies in any format recognized by soundgen: a vector of formant frequencies like c(550, 1600, 3200); a list with multiple values per formant like list(f1 = c(500, 550), f2 = 1200)); or a character string like aaui referring to default presets for speaker "M1" in soundgen presets

method

the method of estimating vocal tract length (see details)

interceptZero

if TRUE, forces the regression curve to pass through the origin. This reduces the influence of highly variable lower formants, but we have to commit to a particular model of the vocal tract: closed-open or open-open/closed-closed (method = "regression" only)

tube

the vocal tract is assumed to be a cylindrical tube that is either "closed-open" or "open-open" (same as closed-closed)

speedSound

speed of sound in warm air, by default 35400 cm/s. Stevens (2000) "Acoustic phonetics", p. 138

checkFormat

if FALSE, only a list of properly formatted formant frequencies is accepted

output

"simple" (default) = just the VTL; "detailed" = a list of additional stats (see Value below)

plot

if TRUE, plots the regression line whose slope gives formant dispersion (method = "regression" only). Label sizes show the influence of each formant, and the blue line corresponds to each formant being an integer multiple of F1 (as when harmonics are misidentified as formants); the second plot shows how VTL varies depending on the number of formants used

See Also

schwa

Examples

estimateVTL(NA)
estimateVTL(500)
estimateVTL(c(600, 1850, 2800, 3600, 5000), plot = TRUE)
estimateVTL(c(600, 1850, 2800, 3600, 5000), plot = TRUE, output = 'detailed')
estimateVTL(c(1200, 2000, 2800, 3800, 5400, 6400),
  tube = 'open-open', interceptZero = FALSE, plot = TRUE)
estimateVTL(c(1200, 2000, 2800, 3800, 5400, 6400),
  tube = 'open-open', interceptZero = TRUE, plot = TRUE)

# Multiple measurements are OK
estimateVTL(
  formants = list(f1 = c(540, 600, 550),
  f2 = 1650, f3 = c(2400, 2550)),
  plot = TRUE, output = 'detailed')
# NB: this is better than averaging formant values. Cf.:
estimateVTL(
  formants = list(f1 = mean(c(540, 600, 550)),
  f2 = 1650, f3 = mean(c(2400, 2550))),
  plot = TRUE)

# Missing values are OK
estimateVTL(c(600, 1850, 3100, NA, 5000), plot = TRUE)
estimateVTL(list(f1 = 500, f2 = c(1650, NA, 1400), f3 = 2700), plot = TRUE)

# Note that VTL estimates based on the commonly reported 'meanDispersion'
# depend only on the first and last formants
estimateVTL(c(500, 1400, 2800, 4100), method = 'meanDispersion')
estimateVTL(c(500, 1100, 2300, 4100), method = 'meanDispersion') # identical
# ...but this is not the case for 'meanFormant' and 'regression' methods
estimateVTL(c(500, 1400, 2800, 4100), method = 'meanFormant')
estimateVTL(c(500, 1100, 2300, 4100), method = 'meanFormant') # much longer

if (FALSE) {
# 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
}