Pomak is an endangered South East Slavic language variety spoken in Nothern Greece.
This is the first automatic speech recognition (ASR) model for Pomak.
To train the model, we fine-tuned a Slavic model (classla/wav2vec2-large-slavic-parlaspeech-hr) on 11h of recorded Pomak speech.
Recordings
Four native Pomak speakers (2 female and 2 male) agreed to read Pomak texts at the ILSP audio-visual studio in Xanthi, Greece, resulting in a corpus of 14h.
Speaker
Gender
Total recorded hours
NK9dIF
F
4h 44m 45s
xoVY9q
M
4h 36m 12s
9G75fk
F
1h 44m 03s
n5WzHj
M
3h 44m 04s
To fine-tune the model, we split the long recordings into smaller segments of a maximum of 25 seconds each.
This removed the majority of pauses and resulted in a total dataset duration of 11h 8m.
Metrics
We evaluated the model on the test set split, which consists of 10% of the dataset recordings.
Model
CER
WER
pre-trained
87.31%
31.47%
fine-tuned
9.06%
3.12%
Training hyperparameters
We fine-tuned the baseline model (wav2vec2-large-slavic-parlaspeech-hr) on an NVIDIA GeForce RTX 3090, using the following hyperparameters:
arg
value
per_device_train_batch_size
8
gradient_accumulation_steps
2
num_train_epochs
35
learning_rate
3e-4
warmup_steps
500
Citation
To cite this work or read more about the training pipeline, see this paper
@inproceedings{tsoukala-etal-2023-asr,
title = "{ASR} pipeline for low-resourced languages: A case study on Pomak",
author = "Tsoukala, Chara and
Kritsis, Kosmas and
Douros, Ioannis and
Katsamanis, Athanasios and
Kokkas, Nikolaos and
Arampatzakis, Vasileios and
Sevetlidis, Vasileios and
Markantonatou, Stella and
Pavlidis, George",
booktitle = "Proceedings of the Second Workshop on NLP Applications to Field Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.fieldmatters-1.5",
doi = "10.18653/v1/2023.fieldmatters-1.5",
pages = "40--45",
abstract = "Automatic Speech Recognition (ASR) models can aid field linguists by facilitating the creation of text corpora from oral material. Training ASR systems for low-resource languages can be a challenging task not only due to lack of resources but also due to the work required for the preparation of a training dataset. We present a pipeline for data processing and ASR model training for low-resourced languages, based on the language family. As a case study, we collected recordings of Pomak, an endangered South East Slavic language variety spoken in Greece. Using the proposed pipeline, we trained the first Pomak ASR model.",
}
