This package constitutes a database of segments in the International
Phonetic Alphabet (IPA) and their equivalents in terms of (articulatory)
phonological features. They include both data files and the tool
generate_ipa_all.py
, which allows the application of rules for
diacritics and modifiers to collections of IPA characters, data files,
and configuration/rule files and well as the tool validate_ipa.py
,
which checks Unicode IPA text from STDIN for well-formedness.
The panphon
module provides a straightforward API that allows users
and developers to access the segment-feature relationships encoded in
the IPA database panphon/data/ipa_all.csv
.
>>> import panphon.panphon as panphon
>>> ft = panphon.FeatureTable()
>>> ft.ftr_match(set([(u'+', u'syl')]), u'a')
True
>>> ft.segs(u'pʲãk')
[u'p\u02b2', u'a\u0303', u'k']
>>> ft.word_fts(u'pʲãk')
[set([(u'-', u'syl'), (u'-', u'long'), (u'-', u'voi'), (u'+', u'ant'), (u'-', u'cg'), (u'+', u'hi'), (u'-', u'son'), (u'0', u'tense'), (u'-', u'lat'), (u'-', u'back'), (u'-', u'cont'), (u'-', u'nas'), (u'-', u'lo'), (u'0', u'distr'), (u'-', u'round'), (u'-', u'delrel'), (u'+', u'lab'), (u'-', u'sg'), (u'+', u'cons'), (u'0', u'strid'), (u'-', u'cor')]), set([(u'+', u'son'), (u'+', u'tense'), (u'+', u'cont'), (u'+', u'nas'), (u'+', u'lo'), (u'+', u'voi'), (u'-', u'cg'), (u'-', u'hi'), (u'-', u'lat'), (u'+', u'syl'), (u'0', u'strid'), (u'-', u'long'), (u'-', u'cor'), (u'0', u'distr'), (u'-', u'round'), (u'-', u'delrel'), (u'0', u'ant'), (u'-', u'sg'), (u'+', u'back'), (u'-', u'cons'), (u'-', u'lab')]), set([(u'-', u'syl'), (u'-', u'lab'), (u'-', u'voi'), (u'0', u'distr'), (u'+', u'back'), (u'-', u'cg'), (u'+', u'hi'), (u'-', u'son'), (u'0', u'tense'), (u'-', u'lat'), (u'-', u'cont'), (u'-', u'nas'), (u'-', u'lo'), (u'-', u'ant'), (u'-', u'round'), (u'-', u'delrel'), (u'-', u'sg'), (u'+', u'cons'), (u'0', u'strid'), (u'-', u'cor'), (u'-', u'long')])]
The FeatureTable
class includes a broad range of operations on
features and segments (consonants and vowels).
The panphon
class includes the function word2array which takes a
list of feature names (as a list of strings) and a panphon word (from
FeatureTable().word_fts()) and returns a NumPy array where each row
corresponds to a segment in the word and each column corresponds to one
of the specified features. Basic usage is illustrated in the following
example:
>>> import panphon
>>> ft=panphon.FeatureTable()
>>> panphon.word2array(['syl', 'son', 'cont'], ft.word_fts(u'snik'))
array([[-1, -1, 1],
[-1, 1, -1],
[ 1, 1, 1],
[-1, -1, -1]])
The FeatureTable
class also allows matching of fixed-width,
feature-based patterns.
The Sonority
class has methods for computing sonority scores for
segments.
The Distance
class includes methods for calculating edit distance,
both in which the cost of substitutions is based upon Hamming distance
between the feature vectors and in which the cost of substitutions are
based upon edit weights for individual features.
This module includes the Distance
class, which includes various
methods for computing the distance between Unicode IPA strings,
including convenience methods (really "inconvenience methods") for
computing Levenshtein distance, but--more importantly--methods for
computing similarity metrics related to articulatory features. The
methods include the following:
panphon.distance.Distance
.levenshtein_distance
A Python implementation of Levenshtein's string edit distance.
panphon.distance.Distance
.fast_levenshtein_distance
A C implementation of Levenshtein's string edit distance. Unsurprisingly, must faster than the former.
panphon.distance.Distance
.dogol_prime_distance
Fast Levenshtein distance after collapsing segments into an enhanced version of Dogolpolsky's equivalence classes.
panphon.distance.Distance
.feature_edit_distance
Edit distance where each feature-edit has cost 1/22. Edits from unspecified to specified cost 1/44.
panphon.distance.Distance
.hamming_feature_edit_distance
Edit distance where each feature-edit has cost 1/22. Edits from unspecified to specified also cost 1/22. Insertions and substitutions each cost 1.
panphon.distance.Distance
.weighted_feature_edit_distance
Edit distance where costs of feature edits are differently weighted depending on their class and subjective variability. All of these methods have the same interface and patterns of usage, demonstrated below:
>>> import panphon.distance
>>> dst = panphon.distance.Distance()
>>> dst.dogol_prime_distance(u'pops', u'bobz')
0
>>> dst.dogol_prime_distance(u'pops', u'bobo')
1
This small Python program allows the user to apply sets of rules, defined in YAML, for adding diacritics and modifiers to IPA segments based upon their phonological features.
To generate a segment features file (ipa_all.csv
), use the following
in the panphon data directory:
$ generate_ipa_all.py ipa_bases.csv -d diacritic_definitions.yml -s sort_order.yml ipa_all.csv
Note that this will overwrite your existing ipa_all.csv
file, which
is often what you want.
[To be added.]
[To be added.]
This package also includes multiple data files. The most important of
these is ipa_bases.csv, a CSV table of IPA characters with definitions
in terms of phonological features. From it, and the
diacritics_definitions.yml
file, the comprehensive ipa_all.csv
is generated.
The IPA Character Table is a CSV file in which the first column contains an IPA segment and each subsequent column contains a phonological feature, coded as +, -, or 0. The features are as follows:
- syl: syllabic
- son: sonorant
- cons: consonantal
- cont: continuant
- delrel: delayed release
- lat: lateral
- nas: nasal
- strid: strident
- voi: voice
- sg: spread glottis
- cg: constricted glottis
- ant: anterior
- cor: coronal
- distr: distributed
- lab: labial
- hi: high (vowel/consonant, not tone)
- lo: low (vowel/consonant, not tone)
- back: back
- round: round
- tense: tense
Inspiration for the data in these tables is drawn primarily from two sources: the data files for HsSPE and Bruce Hayes's feature spreadsheet. It has since be re-rationalizeds based on evidence from a wide range of sources. As such, any special relationship to these prior inspirations has been eliminated.
The IPA Character Table ipa_bases.csv
is intended to contain all of
the unmodified segmental symbols in IPA, as well as all common
affricates and dually-articulated segments. It is meant to be augmented
by the rule-driven application of diacritics and modifiers.
This package includes two files that control the behavior of
generate_ipa_all.py
. These are intended to be edited by the end
user. Both are written in YAML, a
standardized and human-readable and editable data serialization
language.
The file sort_order.yml
controls the ordering of segments in the
output of the Diacritic Application Tool. It is a sequence of maps, each
with two fields:
- name The name of a feature.
- reverse A boolean value (True or False) specifying whether sorting on the named feature will be reversed or not.
The order of the features determines the priority of sorting.
The file sort_order_schema_.yml
is a
Kwalify schema that defines a
syntactically valid sort order file.
The most important file for controlling the Diacritic Application Tool
is diacritic_definitions.yml
, a list of rules for applying
diacritics and modifiers to IPA segments based on their phonological
features. It has two sections, diacritics and combinations. Each
of these is the key to an item in the top-level map.
The key diacritics points to a list of rules for applying diacritics/modifiers to bases. Each rule is a map with the following fields:
- marker. The Unicode diacritic or modifier.
- name. The name of the series derived from applying the diacritic or modifier.
- postion. The position of the diacritic relative to the base (pre or post).
- conditions. A list of conditions, each of them consisting of an associative array of feature specifications, under which the diacritic or modifier will be applied to a base.
- exclude. A sequence of segments to be excluded from the application of the diacritic/modifier even if they match the conditions.
- content. The feature specifications that will be set if the diacritic or modifier is applied, given as a map of feature specifications.
The key combinations likewise points to a list of rules for combining the rules in diacritics. These rules are very simple, and include only the following fields:
- name. The name of the combined category.
- combines. A sequence of the names of the rules from diacritics that are to be combined.
The file diacritic_definitions_schema.yml
is a
Kwalify schema that defines a
syntactically valid diacritics definition file.
If you use PanPhon in research, please cite the following paper:
David R. Mortensen, Patrick Littell, Akash Bharadwaj, Kartik Goyal, Chris Dyer, Lori Levin (2016). "PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors." Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3475–3484, Osaka, Japan, December 11-17 2016.