This article introduces the process of HW-TSC and the results of IWSLT 2024 Indic Track Speech to Text Translation. We designed a cascade system consisting of an ASR model and a machine translation model to translate speech from one language to another. For the ASR part, we directly use whisper large v3 as our ASR model. Our main task is to optimize the machine translation model (en2ta, en2hi, en2bn). In the process of optimizing the translation model, we first use bilingual corpus to train the baseline model. Then we use monolingual data to construct pseudo-corpus data to further enhance the baseline model. Finally, we filter the parallel corpus data through the labse filtering method and finetune the model again, which can further improve the bleu value. We also selected domain data from bilingual corpus to finetune previous model to achieve the best results.

In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.

This paper outlines our submission for the IWSLT 2024 Simultaneous Speech-to-Text (SimulS2T) and Speech-to-Speech (SimulS2S) Translation competition. We have engaged in all four language directions and both the SimulS2T and SimulS2S tracks: English-German (EN-DE), English-Chinese (EN-ZH), English-Japanese (EN-JA), and Czech-English (CS-EN). For the S2T track, we have built upon our previous year’s system and further honed the cascade system composed of ASR model and MT model. Concurrently, we have introduced an end-to-end system specifically for the CS-EN direction. This end-to-end (E2E) system primarily employs the pre-trained seamlessM4T model. In relation to the SimulS2S track, we have integrated a novel TTS model into our SimulS2T system. The final submission for the S2T directions of EN-DE, EN-ZH, and EN-JA has been refined over our championship system from last year. Building upon this foundation, the incorporation of the new TTS into our SimulS2S system has resulted in the ASR-BLEU surpassing last year’s best score.

This paper presents the submission of Huawei Translation Services Center for the IWSLT 2023 dubbing task in the unconstrained setting. The proposed solution consists of a Transformer-based machine translation model and a phoneme duration predictor. The Transformer is deep and multiple target-to-source length-ratio class labels are used to control target lengths. The variation predictor in FastSpeech2 is utilized to predict phoneme durations. To optimize the isochrony in dubbing, re-ranking and scaling are performed. The source audio duration is used as a reference to re-rank the translations of different length-ratio labels, and the one with minimum time deviation is preferred. Additionally, the phoneme duration outputs are scaled within a defined threshold to narrow the duration gap with the source audio.

This paper presents Huawei Translation Service Center (HW-TSC)’s submission on the IWSLT 2023 formality control task, which provides two training scenarios: supervised and zero-shot, each containing two language pairs, and sets constrained and unconstrained conditions. We train the formality control models for these four language pairs under these two conditions respectively, and submit the corresponding translation results. Our efforts are divided into two fronts: enhancing general translation quality and improving formality control capability. According to the different requirements of the formality control task, we use a multi-stage pre-training method to train a bilingual or multilingual neural machine translation (NMT) model as the basic model, which can improve the general translation quality of the base model to a relatively high level. Then, under the premise of affecting the general translation quality of the basic model as little as possible, we adopt domain adaptation and reranking-based transductive learning methods to improve the formality control capability of the model.