Paper Info
Title
GigaSpeech - An Evolving, Multi-Domain ASR Corpus with 10, 000 Hours of Transcribed Audio.
Abstract
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.
Year
DOI
Venue
2021
10.21437/Interspeech.2021-1965
Interspeech
DocType
Citations 
PageRank 
Conference
4
0.38
References 
Authors
0
21
Name
Order
Citations
PageRank
Guoguo Chen142819.89
Shuzhou Chai241.06
Guan-Bo Wang351.75
Jiayu Du440.72
Wei-Qiang Zhang563.39
Chao Weng611319.75
Dan Su77512.37
Daniel Povey82442231.75
Jan Trmal923520.91
Junbo Zhang1041.74
Mingjie Jin1140.38
Sanjeev Khudanpur122155202.00
Shinji Watanabe131158139.38
Shuaijiang Zhao1440.72
Wei Zou15293.89
Xiangang Li16343.65
Xuchen Yao1720814.09
Yongqing Wang1842.07
Yujun Wang194810.48
Zhao You20679.39
Zhiyong Yan2141.74