The vast majority of ASR research uses corpora in which both the training and
test data have been pre-segmented into utterances. In most real-word ASR
use-cases, however, test audio is not segmented, leading to a mismatch between
inference-time conditions and models trained on segmented utterances. In this
paper, we re-release three standard ASR corpora - TED-LIUM 3, Gigapeech, and
VoxPopuli-en - with updated transcription and alignments to enable their use
for long-form ASR research. We use these reconstituted corpora to study the
train-test mismatch problem for transducers and attention-based
encoder-decoders (AEDs), confirming that AEDs are more susceptible to this
issue. Finally, we benchmark a simple long-form training for these models,
showing its efficacy for model robustness under this domain shift.

本文重新发布三个标准的 ASR 语料库，用于长篇 ASR 研究，并研究了训练与测试数据不匹配问题，通过基准测试展示了长篇训练在此领域转变下的模型鲁棒性。

长篇语音识别的更新语料库和基准

Updated Corpora and Benchmarks for Long-Form Speech Recognition

All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that
use a single neural network to transduce audio to word sequences have been
shown to achieve state-of-the-art results on several tasks. In this work, we
examine the ability of E2E models to generalize to unseen domains, where we
find that models trained on short utterances fail to generalize to long-form
speech. We propose two complementary solutions to address this: training on
diverse acoustic data, and LSTM state manipulation to simulate long-form audio
when training using short utterances. On a synthesized long-form test set,
adding data diversity improves word error rate (WER) by 90% relative, while
simulating long-form training improves it by 67% relative, though the
combination doesn't improve over data diversity alone. On a real long-form
call-center test set, adding data diversity improves WER by 40% relative.
Simulating long-form training on top of data diversity improves performance by
an additional 27% relative.

通过多样化训练数据以及 LSTM 状态操作模拟长形音频，可以增强端到端语音识别模型对未知领域数据的泛化能力。