# Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic
Data Formats

Arne Rubehn Chair for Multilingual Computational Linguistics, University of
Passau
Passau, Germany  Jessica Nieder Chair for Multilingual Computational
Linguistics, University of Passau
Passau, Germany  Robert Forkel DLCE, MPI-EVA
Leipzig, Germany  Johann-Mattis List Chair for Multilingual Computational
Linguistics, University of Passau
Passau, Germany

###### Abstract

When comparing speech sounds across languages, scholars often make use of
feature representations of individual sounds in order to determine fine-
grained sound similarities. Although binary feature systems for large numbers
of speech sounds have been proposed, large-scale computational applications
often face the challenges that the proposed feature systems – even if they
list features for several thousand sounds – only cover a smaller part of the
numerous speech sounds reflected in actual cross-linguistic data. In order to
address the problem of missing data for attested speech sounds, we propose a
new approach that can create binary feature vectors dynamically for all sounds
that can be represented in the the standardized version of the International
Phonetic Alphabet proposed by the Cross-Linguistic Transcription Systems
(CLTS) reference catalog. Since CLTS is actively used in large data
collections, covering more than 2,000 distinct language varieties, our
procedure for the generation of binary feature vectors provides immediate
access to a very large collection of multilingual wordlists. Testing our
feature system in different ways on different datasets proves that the system
is not only useful to provide a straightforward means to compare the
similarity of speech sounds, but also illustrates its potential to be used in
future cross-linguistic machine learning applications.

Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic
Data Formats

##  1 Introduction

The past two decades have seen a drastic increase in standardized datasets in
historical linguistics and linguistic typology which are available in both
human- and machine-readable form (Dellert et al., 2020; Skirgård et al., 2023;
Wichmann et al., 2013). With Lexibank (https://lexibank.clld.org, List et al.
2022), a large collection of comparative wordlists has been compiled in which
word forms from various independently published datasets are standardized
along three dimensions, including (1) the languages in which they occur, (2)
the concepts which they express, and (3) the sounds that constitute them.
Lexibank is a result of the more general Cross-Linguistic Data Formats
initiative (CLDF, https://cldf.clld.org, Forkel et al. 2018), which aims to
unify several kinds of cross-linguistic data (wordlists, typological datasets,
interlinearglossed texts) by proposing an exchange format along with
guidelines and recommendations for standardization.

Sounds in Lexibank are represented in a unified transcription system, proposed
as part of the Cross-Linguistic Transcription Systems reference catalogue
(CLTS, https://clts.clld.org, List et al. 2024) that can be considered a large
standardized subset of the International Phonetic Alphabet (IPA, IPA 1999).
The CLTS system tries to handle as much of the variation observed in phonetic
transcriptions as possible, using a dynamic method that parses phonetic
transcriptions in a given transcription system and derives features from
individual symbol combinations (Anderson et al., 2018). The feature system
underlying these transcriptions has been designed in a pragmatic way that
would allow to capture as much of the graphical variation in using the IPA
(and other transcription systems) as possible (Anderson et al., 2023). As a
result, the system is powerful in parsing phonetic transcriptions –
specifically those represented in IPA – but it is not particularly useful to
compare speech sounds with respect to their _similarity_ (be it acoustic or
articulatory or a combination of both).

Thus, while the CLTS system does its job in helping to standardize phonetic
transcriptions in an unprecedented way, as witnessed by the Lexibank
collection (and numerous additional CLDF wordlists that have been published in
the past years), it falls short in providing a reliable means to compare
individual sounds for their similarity.

In this study, we present a very straightforward approach to convert the CLTS
feature system to a vector representation. This approach takes CLTS feature
bundles as input and converts them into binary feature vectors that can be
used for various downstream tasks in computational phonology, computational
historical linguistics, and computational linguistic typology.

##  2 Background

Modeling speech sounds as bundles of distinctive features can be seen as the
most typical and straightforward way in phonology and comparative linguistics
to compare the similarity of speech sounds. It is therefore not surprising,
that phonological features play a crucial role in a number of different
approaches, ranging from historical language comparison (Kondrak, 2000) over
dialectology (Nerbonne and Heeringa, 1997; Hoppenbrouwers and Hoppenbrouwers,
2001) and phonological rule induction (Gildea and Jurafsky, 1996) to child
language acquisition (Somers, 1998). Representing sounds with the help of
features can also enhance the performance of automatic speech recognition
(Metze, 2007) and transliteration (Tao et al., 2006; Yoon et al., 2007), as
well as automatic phonetic transcriptions from text and named entity
recognition (Mortensen et al., 2016). Related studies have additionally
demonstrated that meaningful phone(me) embeddings can be learned from
distributional properties (Silfverberg et al., 2018; Sofroniev and Çöltekin,
2018).

Beyond these mostly implicit uses of phonological features, there exist
several frameworks with the explicit purpose of modeling the interactions
between sounds and their respective features. Computational tools for
analyzing phoneme inventories in terms of phonological features and natural
classes are available in the form of web applications (Steel and Jurgec, 2017)
or downloadable programs (van Vugt and Hayes, 2021). However, these tools by
default cover rather small inventories of fairly common sounds and are often
even only designed for individual languages.

There is a small number of datasets that map a large number of sounds to a
feature representation, aiming to cover a substantially large amount of speech
sounds in order to be applicable in cross-linguistic studies. PanPhon
(Mortensen et al., 2016) defines feature representations for approximately
5,000 sounds, the similar but smaller framework DistFeat (Tresoldi, 2020)
spans roughly 500 sounds. Phonotacticon (Joo and Hsu, 2023), a typological
resource for phonotactics, extends PanPhon to around 20,000 distinct speech
sounds. PHOIBLE (Moran and McCloy, 2019), a database covering various phoneme
inventories, is equipped with feature definitions as well, covering all 3,000
distinct sounds attested in the data in its latest version. Finally, the
Python package ipasymbols (Hamster, 2022) is designed to query IPA symbols by
their articulatory properties, but is not equipped with phonological features
and is currently (v.0.1.0) limited to only 179 sounds.

While all feature collections are much larger than the earlier feature
collections that phonologists proposed for individual languages, reflecting
the trend towards cross-linguistic approaches that allow for a comparison
across multiple languages, all feature collections are _fixed sets of sounds_
, lacking a dynamic component. This limits their potential when applying them
to newly compiled datasets, since whenever a sound in a given dataset is not
attested in the feature systems, users would have to add it or to label it as
missing data.

While this may seem to reflect a minor problem, it has grown into a major
obstacle for many concrete applications in computational comparative
linguistics, since practical experience in working with concrete language data
clearly shows that meeting unobserved sounds when turning to new datasets is
rather the rule than the exception (see the observation in Moran 2012, that
the overall number of distinct speech sounds seems to increase almost
constantly, albeit slowly, when new data are added to the sample). One way to
avoid the problem of observing missing sounds is to arbitrarily extend
mappings from IPA transcriptions of speech sounds to feature mappings in a
systematic way, as exemplified by the extended system proposed by the
Phonotacticon, with 20,000 distinct sounds, of which only a couple of hundred
sounds occur in the final database.

An alternative, more robust approach, specifically important for data
standardized in CLDF, would take the pragmatically oriented non-binary
features provided by the CLTS system as a starting point and convert them to a
binary vector representation.

##  3 Materials and Methods

###  3.1 Materials

The starting point of our approach is the CLTS reference catalogue, which
links feature descriptions of speech sounds in the style of the IPA to
different transcription systems and datasets. While the CLTS website presents
a list of about 8,000 distinct speech sounds that are linked to various
datasets, including PHOIBLE and PanPhon, the system that generates the website
is dynamic, with only a couple of hundred base sounds being defined
explicitly. The rest of the sounds is generated from sound transcriptions
mainly provided in the International Phonetic Alphabet. The dynamic system
underlying the CLTS reference catalogue can be accessed with the help of a
Python API (https://pypi.org/projects/pyclts, List et al. 2020, see Anderson
et al. 2018 for the details regarding the algorithm used by the API). As a
result, IPA strings that are not directly represented in the system can be
processed, as long as they conform to IPA standards (broadly defined by CLTS).
The CLTS system parses sounds in two ways, taking a phonetic transcription
(typically provided in IPA) as starting point, or starting from the typical
name of a speech sound, as they are also defined by the IPA. For example, [p]
would be described as the ‘voiceless bilabial stop consonant’, yielding the
descriptive feature set (‘voiceless’, ‘bilabial’, ‘stop’, ‘consonant’). For
the conversion of sound transcriptions accepted by CLTS to binary feature
vectors, we use the feature bundle representation rather than the phonetic
transcription as our starting point.

In order to evaluate the usefulness of the binary feature vectors derived from
CLTS, we use the Lexibank database, since it provides a large collection of
wordlists that conform to the standard defined by CLTS.

As of version 1.0 (List et al., 2023) Lexibank is available in an aggregated
form in which all datasets that are sufficiently standardized – with all
sounds being interpretable by the dynamic CLTS system – are assembled in a
single CLDF dataset that can be parsed and processed in various ways,
including SQLite (see List and Shcherbakova 2023) or Python (using the CL
Toolkit package, see List and Forkel 2021,
https://pypi.org/project/cltoolkit). In this form, Lexibank covers wordlists
of at least 80 distinct words for about 2,000 distinct language varieties.

###  3.2 Methods

####  3.2.1 Feature System

We define a classical feature space of 39 binary phonological features that
can be present (1) or absent (-1), or non-applicable (0). Strictly speaking,
we therefore employ a notion of ternary, rather than binary features, since
there are three instead of two possible values. However, this is merely an
explicit formalization of the way that binary features are usually treated in
phonology: Not all features can apply to all kinds of sounds. It is therefore
necessary to distinguish absent from non-applicable features by assigning them
different numerical values. To illustrate this, consider the feature
[±strident] which only applies to fricatives and affricates (Zsiga, 2013) – it
is worthwhile to distinguish non-strident fricatives (which could be strident)
from other sounds that do not have this feature at all. This notion of
applicability is frequently found in the literature, and formally makes these
systems ternary rather than binary. Keeping this in mind, we will still refer
to this feature system as binary, given that it is the commonly used term to
describe this kind of feature systems.

The majority of the features we define constitutes a fairly well established
standard inventory, where we strictly follow the definitions by Zsiga (2013).
Nevertheless, to be able to cover a comprehensive range of sounds, some
additions to the feature inventory were required. We incorporate three
additional features [velaric], [hitone], and [hireg] from Mortensen et al.
(2016) to handle clicks and tones. Clicks are assigned [+velaric] on top of
their other features that are derived from their analogous pulmonic stops. The
tonal features [hireg] and [hitone] refer to the broader register, and the
more narrow tone quality within the register – both the high tone [5] and the
mid-high tone [4] belong to the high register and are therefore [+hireg], and
within that register, [5] is the higher tone and is therefore [+hitone]
(whereas [4] is [-hitone] analogously). Since we do not want to assign the
feature value of 0 (non-applicable) to tonal features in tones, however, these
two features only yield 4 possible combinations, insufficient for encoding the
canonical 5 tones. We therefore introduce the supplementary feature [loreg] as
a logical counterpart to [hireg], covering the low and the mid-low tones.

Furthermore, we employ three additional features to represent complex tones.
Tonal features in CLTS are based on a rather schematic representation of
Chao’s numeral coding of complex tones in Chinese dialects (Chao, 1930[2006]).
Thus, the tone [214] is labeled as "contour from-mid-low via-low to-mid-high
tone" in the CLTS name space, with the feature value "from-mid-low" (from the
feature [start]) – representing the number 2, "via-low" (from the feature
[middle]) representing 1, and "to-mid-high" (from the feature [end])
representing 4 directly, while the feature value "contour" (from the feature
[contour] adds additional information that tells us that we are dealing with a
contour tone. In the same way, CLTS assigns the values "rising" and "falling"
to tones like [15] and [51] respectively. In the vector representation, the
contour of a tone is directly translated to the features [contour, rising,
falling] based on their respective CLTS feature. Contour tones with both
rising and falling parts additionally receive [+rising] or [+falling],
indicating the interval between the initial and the middle segment. All
complex tones inherit the features [hireg, hitone, loreg] from their initial
segment. The example tone [214] is therefore represented as [-hireg, +hitone,
+loreg, +contour, -rising, +falling].

Finally, seven more features are defined for representing diphthongs, namely
[backshift, frontshift, opening, closing, centering, longdistance,
secondrounded]. These features are used to model the diphthong’s trajectory
across the vowel space (for a more detailed description see Rubehn, 2022,
41-43). Additionally, each diphthong is assigned the simple vowel features of
its initial segment.

Due to its flexibility, the present system is highly customizable and can be
used to generate different feature systems as well. Users can easily define
their own feature inventories and mappings, according to their individual
needs. The workflow presented in the following section is not dependent on the
specific feature system and definitions that we suggest here.

####  3.2.2 Workflow

Our system generates binary feature vectors for any sound based on its feature
set assigned by CLTS. Again, consider the example [p]: The method does not
depart from the string "p", but from the feature set (‘voiceless’, ‘bilabial’,
‘stop’, ‘consonant’) that can easily be obtained from CLTS. The general
workflow for generating binary feature vectors is outlined in Figure 1.

String Representation [p] Descriptive Features voiceless bilabial stop Vector
$\left[\begin{matrix}-\text{son}\\\ -\text{cont}\\\
+\text{lab}\end{matrix}\right]$CLTSHierarchical Vector Mapping Figure 1:
Workflow of vector creation.

Underlying our system is a simple dictionary structure which maps triplets of
CLTS feature values, CLTS feature domains, and binary feature representations
onto each other. The feature value ‘stop’ for example would be linked to the
domain ‘manner’ (of articulation in consonants) and to the binary feature
representation [-son,-cont].

As a starting point, a zero vector (with the value 0 at every position) of the
size of the defined feature inventory is instantiated, with every position of
the vector corresponding to exactly one binary feature. This vector then is
modified by subsequently processing the features in the CLTS feature set, with
the corresponding binary features overwriting the current value in the vector.

A core principle for the successful modification of feature vectors is that we
sort the CLTS features by a hierarchy of concreteness that determines the
order in which those features are processed. This hierarchy states that the
least specific features get processed first, and the most specific ones get
processed last. In the concrete example of [p], that means that ‘consonant’,
being the least specific feature, is processed first.

This notion of hierarchy is necessary for handling conflicting feature
mappings, since we deliberately allow for values in the vector to be
overwritten by features that are processed later. To exemplify this, consider
the ‘devoiced voiced labio-dental fricative’ [*̊v]: The descriptor ‘voiced’
maps to [+voice], whereas ‘devoiced’ naturally corresponds to [-voice].
However, since diacritics modify the base sound, they should take precedence
over it, and the correct feature that should be assigned is [-voice]. This is
ensured by processing the features according to the hierarchy, which states
that the modification ‘devoiced’ is more concrete and should therefore be
applied after the regular phonation feature ‘voiced’, and can thus overwrite
the previously assigned [+voice] with [-voice].

This notion of hierarchy also conveniently allows for the usage of default
values that can define which features apply at which representation level.
This is important since we distinguish between non-applicable (0) and absent
(-1) features, as discussed in Section 3.2.1. We can therefore define which
set of binary features always applies to a certain group of sounds by
assigning a default value to ensure that applicable features have a non-zero
value. For example, the feature [±lab(ial)] must be defined for all
consonants, which is assured by mapping the CLTS feature ‘consonant’ to
[-lab]. If the consonant is actually labial, this feature will be overwritten
with [+lab], since the place of articulation is always applied after the sound
type. So instead of exhaustively defining [-lab] for every non-labial place of
articulation, we can just define it as a default value for the CLTS feature
‘consonant’ instead. This corresponds to the reading that every consonant is
[-lab] (by default), unless specified otherwise.

The majority of sounds can be handled by this straightforward workflow of
hierarchically mapping CLTS features to their binary feature representations.
However, there are a few more complex cases that require an extra processing
step. For example, the glottal stop [P] has the binary feature [+cg]
(‘constricted glottis’) – however, this feature neither corresponds to
‘glottal’, nor to ‘stop’. It is therefore the combination of both ‘glottal’
and ‘stop’ that triggers [+cg]. The system therefore uses a second dictionary
that allows for the definition of joint feature mappings, where a binary
feature definition is conditioned by a certain combination of CLTS features.

Complex sounds that can alternatively be analyzed as two segments – diphthongs
and consonant clusters – pose a similar challenge. For these cases, CLTS
provides the means of analyzing its individual constituents: The consonant
cluster [kp] can be split into [k] and [p]. The system uses this function to
generate separate feature vectors for the two individual sounds, which then
are combined by assigning the union of positive features to the joint vector.
The resulting feature vector for [kp] therefore contains all positive features
that are attributed to either [k] or [p].

In a similar fashion, feature vectors for diphthongs are based on their
initial segments. For example, [aI] inherits its monophthong vowel features
from the feature definitions for [a]. The additional diphthong features, that
indicate the trajectory of the diphthong, are assigned based on joint feature
definitions: The combination of the CLTS features (‘from_open’, ‘to_near-
close’) maps (among others) to the binary feature [+closing].

###  3.3 Implementation

The approach is implemented in the form of a Python package (soundvectors)
that takes as input the canonical names consisting of feature values that CLTS
generates dynamically for speech sounds in standard IPA transcription and can
be applied in combination with CLTS and the pyclts package, as well as with
the linse package that offers non-generative access to a larger selection of
speech sounds covered by CLTS (https://pypi.org/project/linse, List and Forkel
2024), but also independently of these packages, as long as the feature names
follow the CLTS standards. The package along with the data on which it was
tested is available from the supplementary material accompanying this study.

##  4 Evaluation

We test the usefulness of our proposed system by (1) investigating the vector
similarities for common sounds by calculating cosine similarities and
visualizing them with heatmaps, (2) employing techniques for dimensionality
reduction to visualize the relationships between sounds, (3) mapping the CLTS
sound inventory to binary vectors and analyzing the resulting equivalence
classes, and (4) investigating the power of the system to distinguish speech
sounds observed in phonetically transcribed wordlists.

###  4.1 Vector Similarities

Figure 2: Cosine similarities between consonant vectors generated with our
model. Figure 3: Cosine similarities between vowel vectors generated with our
model.

To test how well our system analyzes a representative sample of common sounds,
we take 25 most common consonants and the 20 most common vowels from Phoible
2.0 (Moran and McCloy, 2019) and use heatmaps to visualize the cosine
similarities similarities of their respective feature vectors (Figures 2 and
3). The heatmaps were generated with the Python library Seaborn (Waskom,
2021), with lighter colours representing higher similarities and darker
colours representing lower similarity scores. The sounds are ordered by their
primary place of articulation, starting from the front and moving to the back
of the mouth. As can be observed in Figure 2, the manner of articulation and
the phonation have a clear impact on the similarity of consonantal feature
vectors: [p] is therefore much more similar to [k] than to [N]. The glides [w]
and [j] are strikingly dissimilar to the rest of the consonants, showing their
well-known intermediate role in between consonants and vowels.

Figure 3 shows that the vowel space follows a strong division into two
clusters which correspond to front and back vowels, with [a] being considered
a front vowel according to the IPA nomenclature. This primary partition
reflects the fact that typologically unmarked front vowels are unrounded, and
back vowels are typically rounded. This naturally translates into a separate
feature which drives front and back vowels further apart in terms of their
vector similarity. Nonetheless, vowel pairs that share the same height such as
[e] and [o] retain a fairly high degree of cosine similarity, indicating that
our vectors are able to adequately reflect and apply vowel features to the
used vowel data set.

###  4.2 Dimensionality Reduction

Figure 4: Two-dimensional reduction of feature vectors using PCA. Figure 5:
Two-dimensional reduction of feature vectors using t-SNE.

We employed two techniques for dimensionality reduction to project the
phonological vector space onto a two-dimensional plane, aiming to gain a
comprehensive understanding of the inherent structure of our vectors. The
employed techniques are principal component analysis (PCA; Figure 4), known
for its ability to reveal global linear structures, and t-distributed
stochastic neighbor embedding (t-SNE; Figure 5; van der Maaten and Hinton
2008), known for capturing local nonlinear patterns in the data. Both
dimensionality reductions were computed in Python using the SciKit-Learn
package (Pedregosa et al., 2011) and visualized with Matplotlib (Hunter,
2007). By employing PCA and t-SNE at the same time, we sought to ensure a
robust and detailed exploration of our vectors, leveraging the complementary
strengths of both of these dimensionality reduction techniques (Anowar et al.,
2021).

Figure 4 and Figure 5 visualize the results after dimensionality reduction.
Both PCA and t-SNE reveal a consistent narrative, grouping similar sounds
together. First and foremost, there is a clear distinction between consonants
and vowels, with semi-vowels positioned either between the two clusters in PCA
or in close proximity to the corresponding vowel cluster in t-SNE. Once again,
this reaffirms the ability of our binary vectors to adequately distinguish
between the sounds in a phonologically informed way.

Focusing on the vowel clusters in Figure 4 and Figure 5, it becomes evident
once again that vowels are primarily divided into (unrounded) front and
(rounded) back vowels, aligning with the well-established phonological
classification of vowel sounds. Shifting attention to the consonant clusters
in both panels, we once more observe that they are primarily grouped by their
place of articulation. In both Figures, the velars [k,g,N] form a rather
isolated cluster, loosely associated with the palatal nasal []. Both
techniques also tend to isolate the glottal sounds [h,P], with t-SNE placing
this sound pair much closer to each other. A distinct picture emerges for the
remaining consonants, the coronals and labials: While PCA seems to bundle all
of them together into a single large cluster, t-SNE forms two distinct
clusters based on their place of articulation, however retaining a certain
proximity between the two. The t-SNE plot also exhibits a compelling
parallelogram symmetry among quadruplets of stops and fricatives in their
voiced and voiceless versions: The alveolars [t,d,s,z] form one such
parallelogram in the two-dimensional projection; and a similar pattern can be
observed for labials [p,b,f,v].

The observed similarities depicted in Figure 2 and Figure 3 as well as the
patterns in the vowel and consonant clusters depicted in Figure 4 and Figure 5
align with established phonological classifications, providing a visual
representation that echoes the theoretical descriptions and classes in
phonological theory to a considerable extent. Our observations confirm the
potential utility of our phonological feature vectors in computational models,
suggesting that the vectors capture meaningful distinctions and relationships
inherent in the sounds of human languages.

###  4.3 Equivalence Classes

The current version of CLTS (v.2.1.0) provides a collection of 8,684 unique
sounds that were observed in its source datasets. Employing our system, these
8,684 sounds map to 5,285 distinct feature vectors. The system is therefore
capable of providing a unique representation for 60.9% of this large sound
inventory, even though it contains a number of very narrow transcriptions, or
aspects that we deliberately chose not to represent in the feature space, such
as suprasegmental properties being attributed to a segment (for example,
putting tones on vowels).

The largest two equivalence classes contain 18 segments respectively, which
are all mid and open-mid vowels. The first class therefore contains sounds
that are based on [@] and [3], including among others [@́, @, ∥+3]. All of
these modifications are deliberately disregarded by our system: Tones are
suprasegmental features that should not be represented as part of a segment,
rhotics lack reliable phonetic correlates (Chabot, 2019), and specifying the
relative tongue position is an overly narrow transcription style that does not
carry distinctive information.

This illustrates the principle of economy, in that we only define as many
features as strictly necessary to keep the individual features meaningful and
avoid feature inflation. The distinctions that are lost by employing this
procedure are extremely narrow in domain, and phonetically not meaningful, as
we will argue in the following section.

###  4.4 Distinctiveness

We investigate the discriminative potential of our system by applying it to
all sound inventories observed in phonetic transcriptions of lexical data in
Lexibank (List et al., 2022, 2023). The aggregated dataset combines numerous
datasets into one unified dataset, spanning over 2,905 language varieties in
total. In Table 1, we report the metrics of how well the sound inventories of
the languages in Lexibank 1.0 can be described by our system. We report the
number of confused sounds per language, that is how many sounds in an
inventory share their feature representation with another sound present in
this inventory. Formally, this is the difference between the size of the sound
inventory, and the number of unique feature representations corresponding to
the inventory.

n confused sounds |  n varieties | Portion
---|---|---
0 | 2,376 | 0.818
$\leq$1 | 2,567 | 0.884
$\leq$2 | 2,648 | 0.912
$\leq$3 | 2,689 | 0.926
$\leq$4 | 2,841 | 0.978
Table 1: Number of language varieties in Lexibank 1.0 with at most n confused
sounds.

The sound inventories of 2,376 varieties, amounting to 81.8% of all varieties
in the dataset, can be represented with full distinctiveness by our system,
meaning that every sound is mapped to a unique feature vector. With 2,841
(97.8%) varieties, the grand share of the dataset’s sound inventories can be
represented with a maximum of four overlapping feature representations. These
overlaps can usually be explained by narrow transcriptions, where the exact
realization of the sound is predictable from the context, or can even hint at
transcription errors or inconsistencies in the source data.

We investigate such sets of overlapping sounds in their context using
concordance lines. This technique is frequently used in corpus linguistics for
visualizing in which contexts a certain word appears. Usually, concordance
lines are generated by aligning the highlighted target word to the center of a
table, and placing the contexts to its left or right respectively (Hunston,
2022, 47).

Figure 6: Concordance line for Spanish transcriptions featuring [M] or [m].

Figure 6 exemplifies the usefulness of concordance lines to analyze the
contexts in which sounds occur. Here, we investigate the instances of the
bilabial and labio-dental nasal consonants [m] and [M] in the Spanish data
from the NorthEuraLex database (Dellert et al., 2020). Both sounds are
represented by identical feature vector, but the data makes a distinction
between them. Analyzing the relevant forms, however, shows that the presence
of [M] can be clearly predicted from the context, since it can only occur
before labio-dental obstruents. This suggests that there is no actual
distinction between these sounds, since the different surface forms can be
explained by a fully predictable assimilation process. Similar cases of
complementary distribution can be observed within the same dataset: In Nanai,
[∥+i] is only found preceding bilabial and alveolar consonants, while [i]
occurs elsewhere; Estonian [k] is transcribed as [*̊k] if preceded by [N]; and
Korean voiceless stops are unreleased in word-final position, leading to pairs
of distinct transcriptions (e.g. [t] - [t]) with a distribution that is
completely predictable by context.

##  5 Discussion

In this study, we introduced a new approach to turn the features for all
sounds covered by the CLTS reference catalogue into numerical feature vectors.
Given that CLTS not only underlies the Lexibank repository, which offers
phonetically transcribed, standardized wordlists for more than 2,000 language
varieties, but is also used in many additional applicatations that make use of
the standards proposed by the CLDF initiative, this means that the binary
feature vectors we propose are directly available for a very large number of
language varieties.

To assess the effectiveness of our approach, we conducted a detailed analysis
using cosine similarity and dimensionality reduction techniques. The resulting
similarity patterns, evident in both PCA and t-SNE plots, align with
established phonological classifications. Notably, the model distinguishes
between vowels and consonants and groups similar sounds based on their place
of articulation. Furthermore, we successfully mapped a substantial inventory
of sounds from CLTS to their respective vectors, covering more than half of
this extensive sound dataset with unique representations. Finally, we
evaluated the distinctiveness of our vector representations by discerning
speech sounds from lexical data in the Lexibank repository. Our system
accurately represented a significant portion of the data, ensuring full
distinctiveness by uniquely mapping each sound to a feature vector.

In conclusion, our approach not only provides a practical solution to address
general limitations of the pragmatic feature system underlying the CLTS
reference catalogue but also offers a flexible approach for representing
phonological features in computational linguistics. By converting CLTS feature
bundles into binary feature vectors, the approach enables researchers to
integrate phonological insights into various computational tasks, ranging from
phonology and historical linguistics to linguistic typology.

For the field of cognitive language modeling, our feature system offers an
enhanced, more precise phonological representation. Nieder and List (2024)
utilized historical sound class representations in their language processing
model (Linear Discriminative Learning, see Nieder and List, 2024) to explore
mutual intelligibility among Germanic languages. Expanding such models with
phonological vector representations instead, may offer new insights into how
speech sounds influence meaning and vice versa, thereby guiding language
processing and language learning.

For historical language comparison, feature representations can be used to
dynamically extend fixed-size scoring matrices in computational tasks such as
phonetic alignment (Kondrak, 2000; List, 2012) or phonological reconstruction
(Bouchard-Côté et al., 2013; Jäger, 2019; Meloni et al., 2021). While state-
of-the-art approaches to phonetic alignment typically deal with the problem of
unseen sounds by resorting to sound class representations that represent
sounds in phonetic transcriptions in small classes of similar sounds ranging
from 10 to about 40 distinct sound classes in total (see List 2014 for details
on sound class systems), feature vectors would offer a much more fine-grained
representation of similarities and differences between individual sounds whose
impacts on alignment quality have not been fully tested so far (an exception
is the feature-based system by Kilani 2020, which requires, however, sound-
feature mappings to be set up manually). For the still unsolved task of
unsupervised phonological reconstruction (List, 2023), a common problem of
those approaches that have been proposed so far is that they cannot propose
sounds in ancestral languages that have not been attested in the descendant
languages. Here, feature vectors might propose a way of handling the unknown,
since the vector representation might well propose feature combinations for
ancestral sounds that are not observed in individual languages, thus creating
unseen sounds from attested sounds. But further tests would be needed to
explore the potential of feature vectors in phonological reconstruction.

In summary, we hope that feature vectors, as they have been introduced here,
will prove useful in advancing computational approaches in linguistics and
integrating linguistic insights into machine learning approaches.

## Supplementary Materials

Data and code of this study are curated on GitHub (https://github.com/cldf-
clts/soundvectors), the soundvectors package is also available via the Python
package repository PyPi (https://pypi.org/project/soundvectors, Version 1.0).

## Limitations

As discussed in Sections 4.3 and 4.4, we want our system to distinguish sounds
that sound differently and avoid lumping them together. Our quantitative and
qualitative analyses show that the system seems to be capable of maintaining a
high degree of distinctiveness, however, it is not guaranteed that all
phonemic contrasts in the world’s languages are represented truthfully. While
our phonological feature vectors are a good approximation to spoken language,
we want to point out that they cannot perfectly reflect phonetic similarity –
some features are intuitively more meaningful than others, which is not
explicitly represented in the vector space; and by extension, similar sounds
might differ in a “disproportionately large number of features” (Kondrak,
2000). Heeringa (2004) shows that employing binary features directly as a cost
function is not superior to plain edit distance to measure phonetic similarity
in dialectal data.

These findings do not undermine the potential of feature systems in analyzing
sounds, but rather show that feature vectors should be processed in some way
and not be taken at face value. In fact, a number of studies have shown the
usefulness of phonological feature vectors for processing sounds in machine
learning approaches (e.g. Staib et al. 2020; Lux and Vu 2022). This emphasizes
the need for a robust system that reliably generates feature vectors for all
IPA segments.

## Acknowledgements

The authors would like to thank Johannes Dellert for fruitful discussions on
the choice of the feature inventory and several feature mappings, as well as
two anonymous reviewers for their helpful comments and suggestions. This
project was supported by the ERC Consolidator Grant ProduSemy (PI Johann-
Mattis List, Grant No. 101044282, see https://doi.org/10.3030/101044282).
Views and opinions expressed are however those of the author(s) only and do
not necessarily reflect those of the European Union or the European Research
Council Executive Agency (nor any other funding agencies involved). Neither
the European Union nor the granting authority can be held responsible for
them.

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