Aligned audio corpora are fundamental to NLP technologies such as ASR and speech translation, yet they remain scarce for underrepresented languages, hindering their technological integration. This paper introduces a methodology for constructing LoReSpeech, a low-resource speech-to-speech translation corpus. Our approach begins with LoReASR, a sub-corpus of short audios aligned with their transcriptions, created through a collaborative platform. Building on LoReASR, long-form audio recordings, such as biblical texts, are aligned using tools like the MFA. LoReSpeech delivers both intra- and inter-language alignments, enabling advancements in multilingual ASR systems, direct speech-to-speech translation models, and linguistic preservation efforts, while fostering digital inclusivity. This work is conducted within Tutlayt AI project (https://tutlayt.fr).
@article{arxiv.2502.18215,
title = {Connecting Voices: LoReSpeech as a Low-Resource Speech Parallel Corpus},
author = {Samy Ouzerrout},
journal= {arXiv preprint arXiv:2502.18215},
year = {2026}
}
Comments
This paper is withdrawn because the LoReSpeech dataset described in Section 2 is not currently available, which affects the reproducibility of the work and the validity of the experimental results