B emba Speech Translation: Exploring a Low-Resource A frican Language (2025)

Abstract: This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2025), low-resource languages track, namely for Bemba-to-English speech translation. We built cascaded speech translation systems based on Whisper and NLLB-200, and employed data augmentation techniques, such as back-translation. We investigate the effect of using synthetic data and discuss our experimental setup.

## introduction low-resource languages face critical limitations due to the scarcity and scattered nature of the available data @xcite . speech translation for low-resource languages involves similar challenges @xcite @xcite , similarly, speech applications for african languages are very limited due to the lack of linguistic resources. for example, bemba is an under-resourced language spoken by over 30% of the population in zambia @xcite . hence, the iwslt shared task on speech translation for low-resource languages aims to benchmark and promote speech translation technology for a diverse range of dialects and low-resource languages. we participated in the bemba-to-english language pair through building cascaded speech translation systems. in other words, we employed whisper @xcite for automatic speech recognition (asr), and @xcite ) for text-to-text machine translation (mt). for asr, we fine-tuned whisper models using two datasets, bembaspeech and big-c. for mt, we fine-tuned the nllb-200 models using the bilingual segments of the big-c dataset, and the "dev" split of the flores-200 dataset. in addition, we augmented the bemba-to-english train-⋆ correspondence: yasmin[at]machinetranslation.io ing data with back-translation of a portion of the tatoeba dataset from english into bemba. the back-translated data was filtered based on crossentropy scores. as the data we used to train our bemba-to-english speech translation models can be categorized into: dataset language train dev test audio big-c bem-eng 82,371 2,782 2,763 ✓ ⃝ bembaspeech bem 12,421 1,700 1,359 ✓ ⃝ flores-200 bem-eng 997 0 1,012 ⃝ tatoeba eng 20,121 0 0 ⃝ ## experiments and results as illustrated by figure 1 , our cascaded systems involve two components, an asr model based on whisper to generate transcriptions and an mt model based on nllb-200 to generate text translation. we experimented with different versions of these models, namely whisper small and medium, and nllb-200 with 600m and 3.3b parameters. our code for data preparation, training, and evaluation is publicly available. 3 flores-200 big-c datasets bt size @xmath0,155 ⃝ ✓ ⃝ 20.96 45.06 55.92 28.17 51.26 53.45 20,121 ✓ ⃝ ✓ ⃝ 19.82 44.09 54.79 28.04 51.20 53.51 20,121 ✓ ⃝ ⃝ 21.67 45.25 55.64 27.82 50.98 53.39 training: we trained our models for 3 epochs, saving the best checkpoint based on the chrf++ score during training on the validation dataset. our training arguments were chosen based on both manual exploration and automatic hyperparameter optimization using the optuna framework @xcite . the most important arguments are a learning rate of 1e-4 and a warm-up ratio of 0.03. inference: for inference, we used faster-whisper foot_3 with the default vad foot_4 arguments, and 5 for the "beam size". the model was quantized with the float16 precision for more efficient inference. evaluation: to evaluate our systems, we calculated bleu @xcite , and chrf++ @xcite , as implemented in the sacrebleu library foot_5 @xcite . for semantic evaluation, we used africomet @xcite . we conducted asr evaluation (cf.