This paper introduces GigaSpeech, an evolving, multi-domain English speech
recognition corpus with 10,000 hours of high quality labeled audio suitable for
supervised training, and 40,000 hours of total audio suitable for
semi-supervised and unsupervised training. Around 40,000 hours of transcribed
audio is first collected from audiobooks, podcasts and YouTube, covering both
read and spontaneous speaking styles, and a variety of topics, such as arts,
science, sports, etc. A new forced alignment and segmentation pipeline is
proposed to create sentence segments suitable for speech recognition training,
and to filter out segments with low-quality transcription. For system training,
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h,
and 10000h. For our 10,000-hour XL training subset, we cap the word error rate
at 4% during the filtering/validation stage, and for all our other smaller
training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the
other hand, are re-processed by professional human transcribers to ensure high
transcription quality. Baseline systems are provided for popular speech
recognition toolkits, namely Athena, ESPnet, Kaldi and Pika.

本论文介绍了一个新的、多领域的英语语音识别语料库 ——GigaSpeech，它包含了来自有声读物、播客和 YouTube 等各种领域的高质量标记音频，提供了五个不同大小的训练子集，并提出了一种新的强制对齐和分割管道来创建适用于语音识别训练的句子段，并过滤出低质量的转录片段。最终在 Athena、ESPnet、Kaldi 和 Pika 等流行的语音识别工具包上，提供了基准系统。

GigaSpeech: 一个拥有 10,000 小时转录音频的不断发展的多领域 ASR 语料库

GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of  Transcribed Audio

We release synth1B1, a multi-modal audio corpus consisting of 1 billion
4-second synthesized sounds, paired with the synthesis parameters used to
generate them. The dataset is 100x larger than any audio dataset in the
literature. We also introduce torchsynth, an open source modular synthesizer
that generates the synth1B1 samples on-the-fly at 16200x faster than real-time
(714MHz) on a single GPU. Finally, we release two new audio datasets: FM synth
timbre and subtractive synth pitch. Using these datasets, we demonstrate new
rank-based evaluation criteria for existing audio representations. Finally, we
propose a novel approach to synthesizer hyperparameter optimization.

该研究介绍了一个包含 10 亿个可合成声音及音频参数的多模态音频语料库，提出了一个新的基于排名的音频表示评估标准，并且采用新的综合方法，提高了合成器参数优化的精度。