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GigaST: A 10,000-hour Pseudo Speech Translation Corpus

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arxiv 2204.03939 v2 pith:FFVLLEDM submitted 2022-04-08 cs.CL cs.SDeess.AS

Rong Ye , Chengqi Zhao , Tom Ko , Chutong Meng , Tao Wang , Mingxuan Wang , Jun Cao This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords corpustranslationgigastspeechtranslatedmakepseudotest
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces GigaST, a large-scale pseudo speech translation (ST) corpus. We create the corpus by translating the text in GigaSpeech, an English ASR corpus, into German and Chinese. The training set is translated by a strong machine translation system and the test set is translated by human. ST models trained with an addition of our corpus obtain new state-of-the-art results on the MuST-C English-German benchmark test set. We provide a detailed description of the translation process and verify its quality. We make the translated text data public and hope to facilitate research in speech translation. Additionally, we also release the training scripts on NeurST to make it easy to replicate our systems. GigaST dataset is available at https://st-benchmark.github.io/resources/GigaST.

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Cited by 2 Pith papers

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  1. SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    SeqPO-SiMT uses sequential policy optimization with a combined quality-and-latency reward to improve simultaneous machine translation, beating supervised fine-tuning on six En-Zh and Zh-En datasets.

  2. Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs

    cs.CL 2025-08 unverdicted novelty 4.0 of 10

    Under matched settings, continuous SSL speech features generally outperform discrete tokens on six spoken language understanding tasks in SpeechLLMs.

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