This paper presents the USTC system for the IWSLT 2023 Dialectal and Low-resource shared task, which involves translation from Tunisian Arabic to English. We aim to investigate the mutual transfer between Tunisian Arabic and Modern Standard Arabic (MSA) to enhance the performance of speech translation (ST) by following standard pre-training and fine-tuning pipelines. We synthesize a substantial amount of pseudo Tunisian-English paired data using a multi-step pre-training approach. Integrating a Tunisian-MSA translation module into the end-to-end ST model enables the transfer from Tunisian to MSA and facilitates linguistic normalization of the dialect. To increase the robustness of the ST system, we optimize the model’s ability to adapt to ASR errors and propose a model ensemble method. Results indicate that applying the dialect transfer method can increase the BLEU score of dialectal ST. It is shown that the optimal system ensembles both cascaded and end-to-end ST models, achieving BLEU improvements of 2.4 and 2.8 in test1 and test2 sets, respectively, compared to the best published system.

This paper describes the submissions of the research group USTC-NELSLIP to the 2023 IWSLT Offline Speech Translation competition, which involves translating spoken English into written Chinese. We utilize both cascaded models and end-to-end models for this task. To improve the performance of the cascaded models, we introduce Whisper to reduce errors in the intermediate source language text, achieving a significant improvement in ASR recognition performance. For end-to-end models, we propose Stacked Acoustic-and-Textual En- coding extension (SATE-ex), which feeds the output of the acoustic decoder into the textual decoder for information fusion and to prevent error propagation. Additionally, we improve the performance of the end-to-end system in translating speech by combining the SATE-ex model with the encoder-decoder model through ensembling.

The rapid development of single-modal pre-training has prompted researchers to pay more attention to cross-modal pre-training methods. In this paper, we propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder. Leveraging hidden-unit as an interface to align speech and text, we can decompose the speech-to-text model into a speech-to-unit model and a unit-to-text model, which can be jointly pre-trained with unpaired speech and text data respectively. Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks. Experimental results show that SpeechUT gets substantial improvements over strong baselines, and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks. To better understand the proposed SpeechUT, detailed analyses are conducted. The code and pre-trained models are available at https://aka.ms/SpeechUT.

This paper describes USTC-NELSLIP’s submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese. We describe both cascaded architectures and end-to-end models which can directly translate source speech into target text. In the cascaded condition, we investigate the effectiveness of different model architectures with robust training and achieve 2.72 BLEU improvements over last year’s optimal system on MuST-C English-German test set. In the end-to-end condition, we build models based on Transformer and Conformer architectures, achieving 2.26 BLEU improvements over last year’s optimal end-to-end system. The end-to-end system has obtained promising results, but it is still lagging behind our cascaded models.

This paper describes USTC-NELSLIP’s submissions to the IWSLT2021 Simultaneous Speech Translation task. We proposed a novel simultaneous translation model, Cross-Attention Augmented Transducer (CAAT), which extends conventional RNN-T to sequence-to-sequence tasks without monotonic constraints, e.g., simultaneous translation. Experiments on speech-to-text (S2T) and text-to-text (T2T) simultaneous translation tasks shows CAAT achieves better quality-latency trade-offs compared to wait-k, one of the previous state-of-the-art approaches. Based on CAAT architecture and data augmentation, we build S2T and T2T simultaneous translation systems in this evaluation campaign. Compared to last year’s optimal systems, our S2T simultaneous translation system improves by an average of 11.3 BLEU for all latency regimes, and our T2T simultaneous translation system improves by an average of 4.6 BLEU.