ALADAN at IWSLT 25 Low-resource A rabic Dialectal Speech Translation Task (2025)

Abstract: We present our IWSLT 2025 submission for the low-resource track on North Levantine Arabic to English speech translation, building on our IWSLT 2024 efforts. We retain last year’s cascade ASR architecture that combines a TDNN-F model and a Zipformer for the ASR step. We upgrade the Zipformer to the Zipformer-Large variant (253 M parameters vs. 66 M) to capture richer acoustic representations. For the MT part, to further alleviate data sparsity, we created a crowd-sourced parallel corpus covering five major Arabic dialects (Tunisian, Levantine, Moroccan, Algerian, Egyptian) curated via rigorous qualification and filtering. We show that using crowd-sourced data is feasible in low-resource scenarios as we observe improved automatic evaluation metrics across all dialects. We also experimented with the dataset under a high-resource scenario, where we had access to a large, high-quality Levantine Arabic corpus from LDC. In this setting, adding the crowd-sourced data does not improve the scores on the official validation set anymore. Our final submission scores 20.0 BLEU on the official test set.

## introduction dialectal arabic speech translation (st) remains one of the most challenging tasks in spoken lan guage processing due to (i) the scarcity of high quality, parallel speech-text resources for non standardized varieties, (ii) high phonetic and or thographic variability among dialects, and (iii) do main mismatches between available corpora (e.g., broadcasts in modern standard arabic) and con versational speech. although endtoend mod els and pretrained encoders have advanced gen eral asr and nmt, most publicly available data still target modern standard arabic (alfetyani et al., @xcite , leaving dialectal variants underresourced. previous iwslt eval uations @xcite have tackled these issues using transfer learning and finetuning strategies, yet a comprehensive solution for multi ple dialects is still lacking. in our iwslt 2024 submission, aladan achieved first place in the levantine arabic task by combining a cascade asr pipeline (tdnnf + zipformer) with finetuned nllb and prompt driven llms (commandr), leveraging a crowd sourced parallel corpus for tunisian and levantine arabic. we also demonstrated that prudent data normalization and a hybrid system combination (rover) yield substantial wer and bleu im provements. building on this success, our iwslt 2025 system introduces two key innovations: we replace last year's zipformer (66m parame ters) with the 253mparameter zipformerlarge to better model longrange dependencies and acoustic nuances in dialectal speech. we also train a single multidialect model instead of deriving dialectspecific asr models via fine tuning. our paper is organized as follows. section 2 de tails the data collection and normalization proce dures. section 3 presents our asr and st models, detailing their architecture, training, finetuning, and performance on levantine datasets and inter nal tests. in section 4, we conclude with a discus sion of future directions for lowresource dialect translation. ## experiments this section describes our experimental settings, used data and results. ## conclusions in this work, we demonstrated that carefully engi neered data collection and model adaptation can substantially advance lowresource dialectal ara bic speech translation. by expanding our crowd sourced parallel corpus to five dialects (tunisian, levantine, moroccan, algerian, egyptian), includ ing rigorous qualification tests and multistage fil tering, we provided rich, targeted material for nmt finetuning. upgrading our acoustic front end to a 253 mparameter zipformerlarge and combining it with tdnnf via rover further drove down wer. on the translation side, fine tuning nllb200 and commandr models, with qlora for the latter, on this multidialect dataset yielded significant bleu and comet gains on our indomain test sets. these findings confirm that combining expanded crowdsourcing with un supervised data augmentation and model scaling is a viable and resourceefficient strategy to boost dialectal arabic translation, even when faced with new domains. however, our experiments with the final submission show that adding this dataset on top of already extensive, highquality corpora we used to train our last year's submission does not im prove bleu and chrf scores on the official vali dation set. this suggests that the crowdsourcing approach is more viable in lowresource scenarios, as the knowledge provided by the crowdsourced dataset might already be covered in the larger cor pora.