def GetPlotnikCode(line):
""" Call arpabet2plotnik -> in oder to get the plotnik vowel classes """
phoneset = cmu.read_phoneset("../cmu_phoneset.txt")
vowelSystem = "NorthAmerican"
trans = line[30]
ac = line[27]
stress = line[28]
phone = extractFormants.Phone()
xmin = float(line[38])
xmax = float(line[39])
phone.label = ac + stress
phone.xmin = xmin
phone.xmax = xmax
i = 0
phones = [phone]
speaker = extractFormants.Speaker()
code, prec_p = plotnik.cmu2plotnik_code(i, phones, trans, phoneset,
speaker, vowelSystem)
return code.split('.')[0]
from shorta import is_tense
# setup for testing old system
from cmu import read_phoneset
from extractFormants import Phone
from plotnik_old import phila_system, cmu2plotnik_code
phoneset = read_phoneset('../cmu_phoneset.txt')
def is_tense_old(word, pron):
try:
i = pron.index("AE1")
except ValueError:
return "irrelevant"
phones = []
for phone in pron:
myphone = Phone()
myphone.label = phone
phones.append(myphone)
trans = word
(code, prec_p) = cmu2plotnik_code(i, phones, trans, phoneset, None, "PHILA")
fm = code.split(".")[1][0]
fp = code.split(".")[1][1]
fv = code.split(".")[1][2]
ps = code.split(".")[1][3]
fs = code.split(".")[1][4]
pc = "3"
pcode = phila_system(i, phones, trans, fm, fp, fv, ps, fs, pc, phoneset)
if pcode == "33":
return True
case = options['case']
outputFormat = options['outputFormat']
outputHeader = options['outputHeader']
formantPredictionMethod = options['formantPredictionMethod']
measurementPointMethod = options['measurementPointMethod']
speechSoftware = options['speechSoftware']
nFormants = int(options['nFormants'])
maxFormant = int(options['maxFormant'])
removeStopWords = options['removeStopWords']
measureUnstressed = options['measureUnstressed']
minVowelDuration = float(options['minVowelDuration'])
windowSize = float(options['windowSize'])
preEmphasis = float(options['preEmphasis'])
multipleFiles = options['multipleFiles']
phoneset = cmu.read_phoneset(phonesetFile)
# make sure the specified speech analysis program is in our path
speechSoftware = checkSpeechSoftware(speechSoftware)
# determine what program we'll use to extract portions of the audio file
soundEditor = getSoundEditor()
# if we're using the Mahalanobis distance metric for vowel formant prediction, we need to load files with the mean and covariance values
if formantPredictionMethod == 'mahalanobis':
means = loadMeans(meansFile)
covs = loadCovs(covsFile)
# put the list of stop words in upper or lower case to match the word transcriptions
newStopWords = []
for w in stopWords:
from shorta import is_tense
# setup for testing old system
from cmu import read_phoneset
from extractFormants import Phone
from plotnik_old import phila_system, cmu2plotnik_code
phoneset = read_phoneset('../cmu_phoneset.txt')
def is_tense_old(word, pron):
try:
i = pron.index("AE1")
except ValueError:
return "irrelevant"
phones = []
for phone in pron:
myphone = Phone()
myphone.label = phone
phones.append(myphone)
trans = word
(code, prec_p) = cmu2plotnik_code(i, phones, trans, phoneset, None,
"PHILA")
fm = code.split(".")[1][0]
fp = code.split(".")[1][1]
fv = code.split(".")[1][2]
ps = code.split(".")[1][3]
fs = code.split(".")[1][4]
pc = "3"
pcode = phila_system(i, phones, trans, fm, fp, fv, ps, fs, pc, phoneset)
case = options['case']
outputFormat = options['outputFormat']
outputHeader = options['outputHeader']
formantPredictionMethod = options['formantPredictionMethod']
measurementPointMethod = options['measurementPointMethod']
speechSoftware = options['speechSoftware']
nFormants = int(options['nFormants'])
maxFormant = int(options['maxFormant'])
removeStopWords = options['removeStopWords']
measureUnstressed = options['measureUnstressed']
minVowelDuration = float(options['minVowelDuration'])
windowSize = float(options['windowSize'])
preEmphasis = float(options['preEmphasis'])
multipleFiles = options['multipleFiles']
phoneset = cmu.read_phoneset(phonesetFile)
# make sure the specified speech analysis program is in our path
speechSoftware = checkSpeechSoftware(speechSoftware)
# determine what program we'll use to extract portions of the audio file
soundEditor = getSoundEditor()
# if we're using the Mahalanobis distance metric for vowel formant prediction, we need to load files with the mean and covariance values
if formantPredictionMethod == 'mahalanobis':
means = loadMeans(meansFile)
covs = loadCovs(covsFile)
# put the list of stop words in upper or lower case to match the word transcriptions
newStopWords = []
for w in stopWords:
