This repository is superseeded by https://github.com/cldf-clts/clts and https://github.com/cldf-clts/pyclts

Cross-Linguistic Transcription Systems

This is an attempt to create a system that allows to translate and compare different phonetic transcription systems.

For starters, try this quick example:

>>> from __future__ import unicode_literals
>>> from pyclts import TranscriptionSystem as TS
>>> bipa = TS('bipa') # broad IPA
>>> asjp = TS('asjpcode') # asjp transcription system
>>> snd1 = bipa['ts']
>>> snd2 = asjp['c']
>>> snd1.name
'voiceless alveolar sibilant affricate consonant'
>>> snd2.name
'voiceless alveolar sibilant affricate consonant'
>>> bipa.translate('ts a ŋ ə', asjp)
'c E N 3'
>>> asjp.translate('C a y', bipa)
'tʃ ɐ j'

You can see that internally, we represent the sounds ts and c, depending on the alphabet from which they are taken.

Another example is to parse a sound that is not yet in our database. We will try to create this sound automatically and infer its features from the set of diacritics and the base sound:

>>> sound = bipa['dʱʷ']
>>> sound.name
'labialized breathy voiced alveolar stop consonant'
>>> sound.generated
True
>>> sound.alias
True
>>> print(sound)
dʷʱ
>>> print(sound.uname)
LATIN SMALL LETTER D / MODIFIER LETTER SMALL W / MODIFIER LETTER SMALL H WITH HOOK
>>> print(sound.codepoints)
U+0064 U+02b7 U+02b1

You can see, since we represent breathy-voice phonation differently, we flag this sound as an alias. Also since it is not yet in our database explicitly coded, we flag it as a "generated" sound. In a similar way, you can generate sounds from their names:

>>> sound = bipa['pre-aspirated voiced aspirated bilabial stop consonant']
>>> print(sound)
ʰbʰ
>>> sound.generated
True
>>> sound.name
'pre-aspirated aspirated voiced bilabial plosive consonant'

Note that this sound probably does not exist in any language, but we generate it from the feature components. Note also that the name that is automatically given for the sound automatically orders how the features are put together to form the sound identifier. In principle, our features bundles are unordered, but we try to decide for some explicit order of features to enhance comparison.

You can also use our transcription data to convert from one transcription system to a given dataset (note that backwards-conversion may not be possible, as transcription data is often limited):

>>> from pyclts import TranscriptionSystem, SoundClasses 
>>> bipa = TranscriptionSystem('bipa')
>>> sca = SoundClasses('sca')
>>> bipa.translate('f a: t ə r', sca)
    'B A T E R'

But the translation can even be done in a much simpler way, by loading the transcription data directly:

>>> sca = SoundClasses('sca')
>>> sca('v a t ə r')
    ['B', 'A', 'T', 'E', 'R']

Basic Structure of the Package

The pyclts package offers three basic types of data generated and managed in Python code:

• transcription systems (pyclts.transcriptionsystems.TranscriptionSystem), a system that can generate sounds
• transcription data (pyclts.transcriptiondata.TranscriptionData): a dataset with a fixed number of sounds
• sound classes (pyclts.soundclasses.SoundClasses): a dataset with a direct mapping from sounds to a concrete character (the sound class)

Transcription data is linked to our transcription system by the grapheme for the B(road) IPA transcription system, which serves as our default, and the name, which follows the IPA conventions with some modifications which were needed to make sure that we can represent sounds that we regularly find in cross-linguistic datasets.

Parsing Procedure