BSTC : A Large-Scale C hinese- E nglish Speech Translation Dataset (2021)

Abstract: This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset. This dataset is constructed based on a collection of licensed videos of talks or lectures, including about 68 hours of Mandarin data, their manual transcripts and translations into English, as well as automated transcripts by an automatic speech recognition (ASR) model. We have further asked three experienced interpreters to simultaneously interpret the testing talks in a mock conference setting. This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems. We have organized simultaneous translation tasks and used this corpus to evaluate automatic simultaneous translation systems.

## introduction in recent years, automatic speech translation (ast) has attracted increasing interest for its commercial potential (e.g., simultaneous interpretation and wireless speech translator). a large amount of research has focused on speech translation @xcite @xcite @xcite and simultaneous translation @xcite @xcite @xcite @xcite . the former intends to convert speech signals in the source language to the target language, and the latter aims to achieve a real-time translation that delivers the speech to the audience in the target language while minimizing the delay between the speaker and the translation. to train an ast model, existing corpora can be classified into two categories: the main difference between these two kinds of corpora lies in the way that the translations are generated. the translations in speech translation corpora are generated based on complete audios or their transcripts, while the translations in simultaneous translation corpora are transcribed from real-time human interpretation. existing research on speech translation mainly focused on the translation between english and indo-european languages 1 , with little attention paid to that between chinese (zh) and english. one of the reasons is the scarcity of public zh↔en from:00:00:00 to:00:00:03 ## dataset description bstc is created to fill the gap in @xmath0, in terms of both size and quality. to achieve these objectives, we start by collecting approximate 68 hours of mandarin speeches from three ted-like content producers: bit foot_1 , tndao.com foot_2 , and zaojiu.com foot_3 . the speeches involve a wide range of domains, including it, economy, culture, biology, arts, etc. we randomly extract several talks from the dataset and divide them into the development and test set. ## experiments in this section, we introduce our benchmark systems based on the dataset. we conduct experiments on speech translation and simultaneous translation, respectively. to preprocess the chinese and the english text, we use an open-source chinese segmenter foot_6 , and moses tokenizer foot_7 . after tokenization, we convert ## conclusion and future work in this paper, we release a challenging dataset for the research on chinese-english speech translation and simultaneous translation. based on this 5 and figure 6 . dataset, we report a competitive benchmark based on a cascade system. in the future, we will expand this dataset, and propose an effective method to develop an end-to-end speech translation model.