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
this https URL.

本文介绍 GigaST 数据集，它是一个大规模的伪语音转换语料库，通过翻译英文 ASR 语料库 GigaSpeech 到德语和中文等加入大量的机器翻译的数据用于训练，最终的 ST 模型在 MuST-C 英语 - 德语基准测试集中获得了最新的最好成果。

GigaST：一份拥有 10,000 小时的伪语音翻译语料库

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

Public sources like parliament meeting recordings and transcripts provide
ever-growing material for the training and evaluation of automatic speech
recognition (ASR) systems. In this paper, we publish and analyse the Finnish
parliament ASR corpus, the largest publicly available collection of manually
transcribed speech data for Finnish with over 3000 hours of speech and 449
speakers for which it provides rich demographic metadata. This corpus builds on
earlier initial work, and as a result the corpus has a natural split into two
training subsets from two periods of time. Similarly, there are two official,
corrected test sets covering different times, setting an ASR task with
longitudinal distribution-shift characteristics. An official development set is
also provided. We develop a complete Kaldi-based data preparation pipeline, and
hidden Markov model (HMM), hybrid deep neural network (HMM-DNN) and
attention-based encoder-decoder (AED) ASR recipes. We set benchmarks on the
official test sets, as well as multiple other recently used test sets. Both
temporal corpus subsets are already large, and we observe that beyond their
scale, ASR performance on the official test sets plateaus, whereas other
domains benefit from added data. The HMM-DNN and AED approaches are compared in
a carefully matched equal data setting, with the HMM-DNN system consistently
performing better. Finally, the variation of the ASR accuracy is compared
between the speaker categories available in the parliament metadata to detect
potential biases based on factors such as gender, age, and education.

本文介绍了芬兰议会 ASR 语料库及相关的 Kaldi-based 数据准备管道，HMM，HMM-DNN 和 AED ASR 配方，以及通过测试集和其他数据集在 ASR 上设置了基准，最后比较了不同议会元数据的 ASR 准确性变化。