# Speech Technologies with Fieldwork Recordings: the Case of Haitian Creole

William N Havard, Renauld Govain, Benjamin Lecouteux, Emmanuel Schang

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William N Havard, Renauld Govain, Benjamin Lecouteux, Emmanuel Schang. Speech Technologies with Fieldwork Recordings: the Case of Haitian Creole. ComputEL 8, 2025, Honolulu (Hawaii), United States. hal-05059590

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Speech Technologies with Fieldwork Recordings:
the Case of Haitian Creole

William N. Havard¹,², Renauld Govain³, Benjamin Lecouteux², Emmanuel Schang¹

¹ LLL, Université d'Orléans, CNRS, 45000 Orléans, France

² LIG, Université Grenoble Alpes, CNRS, Grenoble INP, 38000 Grenoble, France

³ LangSé, Université d'État d'Haïti, Port-au-Prince, Haïti

william.havard@univ-orleans.fr

Abstract

We use 40-year-old digitalised tape-recorded
fieldwork data in Haitian Creole to train a na-
tive self-supervised learning (SSL) model of
speech representation (WAV2VEC 2). We also
use a continued pre-training approach on pre-
trained SSL models of two foreign languages:
the lexifier language – French – and an un-
related language – English. We compare the
performances of these three SSL models, and
of two other foreign SSL models directly fine-
tuned, on an ASR task, where all five models
are fine-tuned on transcribed fieldwork record-
ings in Haitian Creole. Our results show the
best-performing model is the one trained using
a continued pre-training approach on the lex-
ifier language, followed by the native model.
We conclude that the ‘mobilising the archive’-
approach advocated by (Bird, 2020) is a promis-
ing way forward to design speech technologies
for new languages.

# 1 Introduction

Most of the so-called low-resourced languages are
often low-resourced from the perspective of com-
puter scientists only: they often have many re-
sources that were collected over the years by lin-
guists, missionaries, and generally by the commu-
nity of speakers itself (Bird, 2020). The data is
often not readily accessible (i.e. in a digitalised
format), but existent nonetheless. The question we
aim to answer in this paper is the following: how far
can we go with state-of-the-art speech processing
models using *only* fieldwork data? By ‘fieldwork
data’, we mean data that was *not* originally col-
lected to serve as training data for computational
applications (e.g. Automatic Speech Recognition,
ASR), but was collected for linguistic purposes
(e.g. to study dialectal variation). In this paper, we
focus on spoken data in Haitian Creole, consisting
of recorded interviews between linguists and their
collaborators. Haitian Creole is a French-based

Creole (i.e. French is called its lexifier language,
the language that gave Haitian Creole most of its
vocabulary (Hazael-Massieux, 2012)), spoken by
13M speakers (Simons and Fennig, 2023) in Haiti
and by the Haitian diaspora.

Most of the data we use in this paper (see Section
2) was collected 40 years ago with tape recorders
to study dialectal variation in Haitian, with a fo-
cus on lexical variations. Contrary to the clean
audiobooks commonly used to train neural models
(e.g. Librispeech, (Panayotov et al., 2015)), the data
we used is inherently noisy: reverberated, echo-y,
full of environmental noise (e.g. chickens, cars,
passers-by, etc.). Yet, this type of data represents
the majority of the data available for most of the
world languages. As collecting and transcribing
data is a costly process (Himmelmann, 2018), is
it possible to make use — as advocated by (Bird,
2020) in the ‘mobilising the archive’-approach —
of already existing (and potentially old) fieldwork
data and re-purpose them for computational appli-
cations?

1.1 Related Works

The field of speech processing for Creole languages
is relatively sparse, except for the work of (Bre-
iter, 2014) for Haitian Creole, that of (Macaire
et al., 2022; Le Ferrand et al., 2023; Le Ferrand
and Prud'hommeaux, 2024) for Guadeloupean and
Mauritian Creole, and (Gooda Sahib-Kaudeer et al.,
2019) for Mauritian Creole (with a focus on the
medical domain). Hence, speech processing for
Creole languages — whatever the lexifier language,
be it French, English, Portuguese, etc. — remains
largely unexplored.

Unrelated to Creole languages — but related to
our experimental settings — (Nowakowski et al.,
2023) explored continuous pre-training (CPT) ap-
proaches, followed by an ASR fine-tuning task for
Ainu speech recognition using old fieldwork data.
In short, CPT is a form of transfer learning which
consists in using large quantities of unlabelled data
(i.e. raw speech) to continue to pre-train models
that were already pre-trained on another language.
However (Nowakowski et al., 2023) do not train
their models ‘on a budget’ as (i) they use 4 GPUs
and (ii) use the XLSR-53 model (Conneau et al.,
2021) which is based on WAV2VEC 2-LARGE and
pre-trained on 56k hours of data, and (iii) use mul-
tilingual fine-tuning by which the ASR model is
not only trained on the target language (Ainu), but
on several languages at once (English, Japanese,
alongside Ainu). We aim for a stricter approach
that only uses fieldwork data at all steps.

## 1.2 Research Questions

In this work, **we only assume the existence of (potentially old) fieldwork data** to train the models, which corresponds to several real-world use cases: that of field linguists documenting a language and that have gathered a certain amount of both untranscribed and transcribed recordings (our case), or that of a community of speakers that uses archival material to build models for their language.

More precisely, the questions we tackle in this
paper are the following: (a) Would noisy, but eco-
logically valid, fieldwork data be usable to train
self-supervised learning (SSL) models of speech
(e.g. WAV2VEC 2, (Baevski et al., 2020))? (b)
Should said models be trained from scratch or
should continued pre-training (CPT) (Gururangan
et al., 2020; Nowakowski et al., 2023) approaches
be used? (c) How much training data is necessary
to fine-tune the models on an ASR task? And fi-
nally, (d) given our use-cases, is it possible to train
such models ‘on a budget’? (i.e. only 1 GPU, as
having more – e.g. 64 as (Baevski et al., 2020) – is
generally impossible for laypeople).

Additionally, as we work in the context of Creole
languages, we also aim to explore the influence
of the lexifier language (as a clear case of related
languages) and (e) whether CPT be done on SSL
models of the lexifier language (e.g. French in the
case of Haitian Creole), or do models trained on an
unrelated language (e.g. say English in the case of
Haitian Creole) also work?

## 2 Data

**ALH.** We used the *Atlas Linguistique d’Haïti*
(Fattier, 1998), which consists of a collection of
499 audio recordings in Haitian Creole (Kreyòl ay-
isyen) collected in Haiti between 1978 and 1987

for the purpose of creating a linguistic atlas. The
recordings were originally done on audio cassettes
with tape recorders which were then digitalised
by the French National Library (Bibliothèque Na-
tionale de France, BNF) in 2010. Each recording
is on average 45 minutes long and features one
or several interviewers eliciting words or phrases
from their native collaborators. This data has been
made publically available by the BNF and is ac-
cessible on the COCOON <fnref n="1" /> platform. Although the
recordings are associated with field notebooks con-
taining partial handwritten transcriptions (phonetic
transcription at word level), these have not been
digitised (nor aligned with the recordings). As a
result, this corpus consists entirely of raw speech.
We partitioned the data set (356.3 hours) into 3
splits (train/val/test). The data was partitioned so
that the validation set would contain at least 5 hours
of data and a minimum of 2 unseen speakers, and
the test set at least 5 hours of data and a minimum
of 3 unseen speakers. We reached the following
distribution which fulfilled our constraints: train =
345.6 hours; val = 5.3 hours, 5 unseen speakers;
and test = 5.4 hours, 8 unseen speakers, the latter
being left for future work.

CNCH. The *Corpus of Northern Haitian Creole*² (Valdman et al., 2015) consists of 10 recorded interviews, conducted in Cap-Haïtien (Northern Haiti) to study dialectal variation with regard to standard Haitian. This corpus was entirely transcribed by the linguist who collected it. However, the transcriptions used are non-standard and impressionistic, in the sense that spelling variations deviating from the norm are used to transcribe the speaker's pronunciation more faithfully: "Powoprens"/"Potoprens", Port-au-Prince, the capital city of Haiti; "eskeu"/"eske", question words; "deu"/"de", two; etc.). We partitioned the data set (9.0 hours) into 3 splits (train/val/test). The data was partitioned so that the val set would contain at least 1 hour of data and a minimum of 1 unseen speaker, and the test set at least 1 hour of data and a minimum of 1 unseen speaker. We reached the following distribution which fulfilled our constraints: train = 6.9 hours; val = 1.1 hours, 1 unseen speaker; test = 1.0 hours, 2 unseen speakers.

**Other data sets.** The two previous data sets are
the only publicly available data sets of fieldwork
recordings in Haitian Creole. We however wish to

<footnote n="1">¹https://cocoon.huma-num.fr/
²https://archive.org/details/
interview-8-ujf-107-a-ujm-107-a</footnote>
acknowledge the existence of other data sets fea-
turing speech in Haitian Creole, which we purpose-
fully excluded during the training phase as they do
not consist of fieldwork data: the freely accessible
Haiti-CMU data set<fnref n="3" /> which features read speech
(~ 20 hours), mainly from sections of the Bible,
which do not reflect everyday language use; and
the proprietary IARPA-Babel data set consisting
of "203 hours of Haitian Creole conversational and
scripted telephone speech" (Andrus et al., 2017).
We use both data sets to test our models on out-of-
domain data and compare them with Facebook's
MMS model (Pratap et al., 2023). For Haiti-CMU,
we generated a test set that consists of 2 hours
of data by randomly sampling recordings; and for
IARPA-Babel we used the development set as a
test set (as it is commonly done with IARPA-Babel
data sets, as the evaluation set was kept private),
which consists of 20 hours of data.

3 Experimental Settings

Given our low-budget setting, we focus on the
WAV2VEC 2-BASE architecture, thus excluding fine-
tuning a multilingual model such as XLSR-53
which is based on the LARGE architecture.

## 3.1 Native and Foreign-SSL Pre-Training

We use the ALH corpus to pre-train our SSL mod-
els. A voice activity detection model (Pyannote
(Bredin et al., 2020)) was used to isolate sections
corresponding to speech from surrounding noises,
resulting in 229h of spoken sections. The resulting
segments were rather short (~ 2.3s) and unsuited to
pre-train SSL models as-is. Thus, we merged them
until the resulting concatenated segments reached
19s on average (19.4±5.8). The WAV2VEC 2 mod-
els were trained on a single GPU⁴ using gradient
accumulation for 16 steps (to simulate 16 GPUs)
with 16-bits floats and a maximum batch size of 5.2
minutes. All the models were implemented using
fairseq's standard WAV2VEC 2-BASE implementa-
tion and training pipeline (Ott et al., 2019). Three
models were trained:

* One model pre-trained from scratch (i.e. not based on any existing pre-trained model):
  - `NATIVE -HAT-SSL+Ø`: this model was pre-trained on the ALH data and has

never been exposed to any other lan-
guage other than Haitian (HAT) through-
out pre-training;

* Two models pre-trained using a continued pre-training approach:
  - `FOREIGN -FRA-SSL+C PT`: the base model was pre-trained on a French (FRA) (wav2vec2-FR-7K-base, pre-trained on 7k hours in French (Parcollet et al., 2023)) and was continued pre-trained on the ALH data;
  - `FOREIGN -ENG-SSL+C PT`: the base model was pre-trained on English (ENG) (wav2vec2-base pre-trained on Librispeech 960 (Baevski et al., 2020)) and was continued pre-trained on the ALH data.

3.2 ASR fine-tuning

We fine-tuned our pre-trained and continued pre-
trained models on the CNCH data set. 5 models
were fine-tuned:

* Three models from models that had seen Haitian speech in a (continued) pre-training phase:
  - `NATIVE -HAT-SSL+Ø +F T`: where `NATIVE -HAT-SSL+Ø` was fine-tuned after the pre-training phase on ALH;
  - `FOREIGN -FRA-SSL+C PT+FT`: where `FOREIGN -FRA-SSL+C PT` was fine-tuned after the continued pre-training phase on ALH;
  - `FOREIGN -ENG-SSL+C PT+FT`: where `FOREIGN -ENG-SSL+C PT` was fine-tuned after the continued pre-training phase on ALH;

* Two models from models that hadn't seen any Haitian speech before being fine-tuned:
  - `FOREIGN -FRA-SSL+Ø +F T`: where the French (wav2vec2-FR-7K-base) was directly fine-tuned.
  - `FOREIGN -ENG-SSL+Ø +F T`: where the English (wav2vec2-base) was directly fine-tuned.

In order to understand the impact of the training
size on the final performance of the models, we
use different train sizes: max (360minutes), 320

<footnote n="3">³http://www.speech.cs.cmu.edu/haitian/
⁴32Gb Nvidia Tesla V100 or 45Gb Nvidia A40 depending on availability.</footnote>
<table><caption>Table 1: Configurations that yield the best performances in terms of WER (left) and CER (right) for each type of fine-tuned model. *Rank* shows the models' rank (from 1/best to 200/worst) when WER/CER is used as sorting key.</caption><thead><tr><th>Model Type</th><th>WER ↓</th><th>CER ↓</th><th>Train Size</th><th>Decoding</th><th>Rank</th></tr></thead><tbody><tr><td>FOREIGN -FRA-SSL+C PT+FT</td><td><b>36.8</b></td><td>21.6</td><td>320</td><td>4-gram</td><td><b>1</b></td></tr><tr><td>NATIVE -HAT-SSL+Ø +F T</td><td>37.4</td><td><b>21.5</b></td><td>360 (max)</td><td>3-gram</td><td>5</td></tr><tr><td>FOREIGN -ENG-SSL+C PT+FT</td><td>37.5</td><td>22.4</td><td>320</td><td>4-gram</td><td>6</td></tr><tr><td>FOREIGN -FRA-SSL+Ø +F T</td><td>42.5</td><td>24.5</td><td>360 (max)</td><td>3-gram</td><td>27</td></tr><tr><td>FOREIGN -ENG-SSL+Ø +F T</td><td>50.4</td><td>29.0</td><td>320</td><td>3-gram</td><td>49</td></tr></tbody></table>

<table><caption>Table 2: Comparison between Facebook's MMS (Pratap et al., 2023), our best-performing model (FOREIGN - FRA-SSL+Ø +F T) and a native model (NATIVE -HAT-SSL+Ø +F T). For a fair comparison between models, only Viterbi decoding was used. Note that MMS was pre-trained on the IARPA-Babel data.</caption><thead><tr><th>Corpus</th><th>Model</th><th>CER ↓</th></tr></thead><tbody><tr><td rowspan="3">CNCH</td><td>FOREIGN -FRA-SSL+C PT+FT</td><td><b>17.1</b></td></tr><tr><td>NATIVE -HAT-SSL+Ø +F T</td><td>17.8</td></tr><tr><td>MMS (Pratap et al., 2023)</td><td>28.4</td></tr><tr><td rowspan="3">Haiti-CMU</td><td>FOREIGN -FRA-SSL+C PT+FT</td><td>09.5</td></tr><tr><td>NATIVE -HAT-SSL+Ø +F T</td><td>11.6</td></tr><tr><td>MMS (Pratap et al., 2023)</td><td><b>07.9</b></td></tr><tr><td rowspan="3">IARPA-Babel</td><td>FOREIGN -FRA-SSL+C PT+FT</td><td>36.6</td></tr><tr><td>NATIVE -HAT-SSL+Ø +F T</td><td>38.5</td></tr><tr><td>MMS (Pratap et al., 2023)</td><td><b>34.6</b></td></tr></tbody></table>

160, 80, 40, 20, 10, and 5 minutes. Each train size including the previous sizes (e.g. $max \supset ... \supset 10 \supset 5$). Each model is fine-tuned for 20k steps<fnref n="5" /> with a CTC loss and the best model is selected on the lowest WER on the validation set. To prevent overfitting, the parameters were frozen for the first 10k steps. The text was lower-cased and diacritics removed (due to inconsistent use).

Finally, we also train 2-to-5-gram LMs using KenLM (Heafield, 2011), with default Kneser-Ney discounting parameters. LMs were trained on the transcriptions of the CNCH data set only (hence, preserving our 'fieldwork data'-only setting). We trained a separate LM for each size of the training data set (e.g. a LM trained on train-10 only uses the text corresponding to the transcription of 10 minutes of speech), resulting in 32 different LMs (4 n-gram sizes × 8 train sizes) that will be used to compare raw (i.e. Viterbi) decoding and LM-rescored decodings.

## 4 Results & Discussion

**Results.** We used the SCTK toolkit<fnref n="6" /> to compute standard Word Error Rate (WER) and Character Error Rate (CER). Standard

<table><thead><tr><th>Model Type</th><th>WER ↓</th><th>CER ↓</th><th>Train Size</th><th>Decoding</th><th>Rank</th></tr></thead><tbody><tr><td>FOREIGN -FRA-SSL+C PT+FT</td><td><b>38.2</b></td><td><b>17.1</b></td><td>320</td><td>Viterbi</td><td><b>1</b></td></tr><tr><td>NATIVE -HAT-SSL+Ø +F T</td><td>39.8</td><td>17.8</td><td>360 (max)</td><td>Viterbi</td><td>3</td></tr><tr><td>FOREIGN -ENG-SSL+C PT+FT</td><td>40.3</td><td>18.6</td><td>360 (max)</td><td>Viterbi</td><td>6</td></tr><tr><td>FOREIGN -FRA-SSL+Ø +F T</td><td>46.2</td><td>21.7</td><td>360 (max)</td><td>Viterbi</td><td>12</td></tr><tr><td>FOREIGN -ENG-SSL+Ø +F T</td><td>57.1</td><td>26.6</td><td>360 (max)</td><td>Viterbi</td><td>38</td></tr></tbody></table>

Viterbi decoding, and LM rescoring with 2-to-5-gram LMs was used. This resulted in 5 fine-tuned models × 8 training sizes × (1 Viterbi + 4 ngram) decoding = 200 decoding strategies. A general overview of our results is shown in Fig. 1 (for clarity, only Viterbi, and 5-gram LM rescoring is shown) and the best configuration for each of the 5 model types is shown in Tab. 1. A performance comparison between Facebook's MMS and our models is shown in Tab. 2.

**Using Fieldwork Data.** Turning back to our original research questions, our results show that (d) it is possible to train competitive models on a budget using a single GPU and that (a) using fieldwork data to train SSL models of speech is effective. Despite such data being inherently noisy — as opposed to audiobooks or broadcast speech commonly used to train SSL models — the NATIVE -HAT-SSL+Ø Haitian model we trained remained very competitive compared to other approaches. This is particularly interesting in the case of low-resourced languages, such as are most of the French-based Creole languages spoken in the Caribbean (Haitian, Guadeloupean, Saint Lucian, etc.) or in South America (Guianan). This means that *no new data needs to be collected*, but that old tape-recorded fieldwork data, once digitalised, can be repurposed for this matter. This opens an avenue for many languages of the world to have cutting-edge speech processing models at their disposal.

**Train From Scratch or Use CPT.** Now, turning to whether we should fine-tune SSL models that have been pre-trained from scratch or models pre-trained using a CPT approach (b), our results show that the CPT models show a slight advantage over native models trained from scratch (–1.6 WER points, and –0.7 CER points, Viterbi decoding, using lowest CER as sorting key). However, our result show that (e) this advantage is only true when the model used for continued finetuning is *that of the lexifier language* (here, French). This advantage seems to disappear when it is not the case, as

<footnote n="5">⁵Given how little data we have, the models quickly converge and remain stable and do not evolve after 20k steps, hence this cutoff value.</footnote>

<footnote n="6">⁶https://github.com/usnistgov/SCTK</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: (a, b) Word Error Rate (WER) and (c, d) Character Error Rate (CER, at the character level) of fine-tuned models on an ASR task with Viterbi decoding (left) and with 5-gram LM (right) as a function of the amount of CNCH data used for training (in minutes, from 5 to *max*, where *max* = 6.9 hours, ~ 360 minutes).</figcaption></figure>

the model fine-tuned from another language (here, English) has generally worse performances than either a model fine-tuned from the lexifier language (+2.1 WER, +1.5 CER, *id.* ) or from the native language (+0.5 WER, +0.8 CER, *id.*). However, what seems most critical is the CPT approach. The ASR models directly fine-tuned from SSL models (FOREIGN -SSL+Ø+F T) that have *not* seen any Haitian speech in a CPT setting lag far behind (+8 WER, +4.6 CER for the French-based models, *id.* ) or very far behind the best model (+18.9 WER, +9.5 CER for the English-based models, *id.*).

**Amount of Fine-tuning Data.** Turning to (c) and the amount of data necessary to fine-tune SSL models on an ASR task, our results show a marked difference between three groups of models: (i) FOREIGN -SSL+C PT+FT very robust to a reduced amount of training data, (ii) FOREIGN -SSL+Ø +FT not very robust to a reduced amount of data, and (iii) NATIVE -HAT-SSL+Ø showing in between results. Using 20 minutes of data closes the gap between (i) and (iii) while models in group (ii) required approximately 4 times this amount of data (80 minutes) to reach similar performances. We hypothesise that models in group (i) benefit from having seen more speech altogether, as they were pre-trained in their respective language (French or English), have seen Haitian data in the CPT phase, and were further fine-tuned, which could explain why they are more robust.

**Viterbi or LM Decoding.** Finally, we observed

mixed results with the use of LMs for decoding. While they do not significantly improve (nor hurt) the NATIVE -HAT-SSL+Ø +F T or FOREIGN -SSL+C PT+FT models, they significantly improved the WER scores of the FOREIGN -SSL+Ø +F T (Fig. 1a and 1b): e.g. -10 WER with a 5-gram LM for FOREIGN -ENG-SSL+Ø +F T model fine-tuned with 40 minutes of data. Hence, when no pre-training data is available and that foreign models can only be directly fine-tuned, using LM-rescoring is indispensable. However, it seems that using LMs, while improving WER scores, comes at the expense of higher CERs (Fig. 1c and 1d); which hints at the fact that while there are more words accurately transcribed, the others are less well transcribed resulting in a higher CERs.

**Comparison with MMS.** Tab. 2 shows a comparison of the performances between our models and Facebook's MMS (Pratap et al., 2023) model with the Haitian adapter. To ensure a fair comparison, only Viterbi decoding was used. MMS obtains better scores (-1.6 CER for Haiti-CMU, and -2 CER for IARPA-Babel) compared to our best-performing model FOREIGN -FRA-SSL+C PT+FT (though, the comparison is not entirely fair, as MMS was pre-trained on the IARPA-Babel data). However, both FOREIGN -FRA-SSL+C PT+FT and NATIVE -HAT-SSL+Ø +F T obtain better CERs than MMS on fieldwork data (-11.3 and -10.6 respectively). This shows that our models are very competitive compared to MMS, particularly given
the fact that MMS was pre-trained on 491k hours
of data and fine-tuned 44.7k hours of labelled data
(including roughly 20 hours of Haitian). In con-
trast, our models are pre-trained on 340 hours of
data and fined-tune on less than 6 hours of data.
It also shows that using fieldwork recordings does
not hinder zero-shot adaptation to out-of-domain
(i.e. non-fieldwork) data, contrary to MMS which
performs much worse on out-of-domain fieldwork
data.

# 5 Limitations and Future Work

In this paper, we focused on exploring the validity
of using fieldwork data to pre-train self-supervised
models to ultimately fine-tune ASR models from
them (extrinsic evaluation), but have left aside the
study of the pre-trained models and representations
themselves (intrinsic evaluation). In future works,
we wish to use an ABX task (Schatz et al., 2013) to
compare the latent representations and their trans-
fer at the phoneme level. This would help us gain
more insight into the performances of our models.
The data we use for continued pre-trained was col-
lected 40 years ago, and the language between that
time and now has evolved (e.g. its phonology, etc.).
Hence, the question of the impact of the diachronic
shift and how to measure it is open. Finally, our
results show that 350 hours of fieldwork record-
ings is enough to pre-train a native SSL model and
obtain competitive results when fine-tuned on an
ASR task. Yet, such a treasure trove with as many
recording hours might not exist for all languages:
the question of the minimal amount of fieldwork
data to use is open.

# 6 Conclusion

In this work, we used 40-years old digitalised tape-
recorded fieldwork data in Haitian to train SSL
models. We trained a native SSL model, and also
used a CPT approach on pre-trained SSL models
of the lexifier language (French) and of an unre-
lated language (English), which we fine-tuned on
another data set of fieldwork recordings on an ASR
task. We obtained competitive results and showed
that the best model is the pre-trained model of the
lexifier language with CPT on Haitian fieldwork
recordings, followed by the native SSL model, ob-
taining close results. Hence, when no model of the
lexifier language is available, it is still worth train-
ing a native model with fieldwork data. Being able
to train a native model is all the most important,

as a native model might be a matter of self-pride
to the speaker community, as opposed to a model
derived from the lexifier language, generally that
of the former colonising power.

Contrary to the work of (Nowakowski et al., 2023), ours is the first that demonstrates the feasibility of training SSL models using **only** fieldwork recordings, and their usability on downstream tasks, such as ASR. This methodology opens an avenue for many languages of the world to have cutting-edge speech-processing models at their disposal, by digitalising recordings collected decades ago. Hence, the ‘mobilising the archive’-approach advocated by (Bird, 2020) constitutes a promising way forward.

The best-performing foreign & native models
will be made public, along with the scripts used to
format the data.

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