emuR (version 2.4.0)

emuR-package: emuR - Main Package of the EMU Speech Database Management System

Description

The emuR package provides the next iteration of the EMU Speech Database Management System with database management, data extraction, data preparation and data visualization facilities.

Details

This package is part of the next iteration of the EMU Speech Database Management System (EMU-SDMS) which aims to be as close to an all-in-one solution for generating, manipulating, querying, analyzing and managing speech databases as possible. For an overview of the system please visit this URL: http://ips-lmu.github.io/EMU.html.

It can be viewed as the main component of the EMU-SDMS as it acts as the central instance that is able to interact with every component of the system. It takes care of database managing duties by being able to interact with a speech database that is stored in the emuDB format. Further, it has easy to understand and learn yet expressive and powerful querying mechanics, that allow the user to easily query the annotation structures of the database. Lastly it provides easy data extraction capabilities that extract data (e.g. formant values) which corresponds to the result of a query.

For an introduction to the emuR package please see the emuR_intro vignette by calling: vignette('emuR_intro')

For information about the emuDB database format please see the emuDB vignette by calling: vignette('emuDB')

For information about the query language used by the EMU-SDMS please see the EQL vignette by calling: vignette('EQL')

Typical work-flow in emuR (emuDB required):

1. Load database into current R session - load_emuDB

2. Database annotation / visual inspection - serve and connect the EMU-webApp to the local server

3. Query database - query (sometimes followed by requery_hier or requery_seq)

4. Get trackdata (e.g. formant values) for the result of a query - get_trackdata

5. Data preparation

6. Visual data inspection

7. Further analysis and statistical processing

TIP: for a browsable overview of all the functions provided by emuR simply run the command help.start() -> click on packages -> click on emuR

References

Harrington, J. (2010). The Phonetic Analysis of Speech Corpora. Blackwell.

Examples

if (FALSE) {
# create demo data including an emuDB called "ae" 
create_emuRdemoData(dir = tempdir())

# construct path to demo emuDB
path2ae = file.path(tempdir(), "emuR_demoData", "ae")

# load emuDB into current R session
ae = load_emuDB(path2ae)

# query loaded emuDB
lvowels = query(ae, "Phonetic = i: | u: | o:")

# extract labels from query result 
lvowels.labs = label(lvowels)

# list all ssffTrackDefinitions of emuDB
list_ssffTrackDefinitions(ae)

# get formant trackdata defined in ssffTrackDefinitions "fm" for query result
lvowels.fm = get_trackdata(ae, lvowels, "fm")

# extract track values at temporal midpoint of segments
lvowels.fmCut = dcut(lvowels.fm, .5, prop = TRUE)

# Plot the data as time signal and formant card
dplot(lvowels.fm[,1:2], lvowels.labs, normalise=TRUE, main = "Formants over vowel duration")
eplot(lvowels.fmCut[,1:2], lvowels.labs, dopoints=TRUE, 
      doellipse=FALSE, main = "F1/F2 of vowel midpoint", form=TRUE, 
      xlab = "F2 in Hz", ylab = "F1 in Hz")
      
      
# Plot of spectral data from 50% of aspiration duration
hs = query(ae,"Phonetic = H")
hs.labs = label(hs)
hs.dft = get_trackdata(ae, hs, "dft")
hs.dftCut = dcut(hs.dft, .5, prop=TRUE)
plot(hs.dftCut, hs.labs, main = "Spectral data of aspiration")

}