  Building Automatic Speech Recognition (ASR) systems from scratch is
significantly challenging, mostly due to the time-consuming and
financially-expensive process of annotating a large amount of audio data with
transcripts. Although several unsupervised pre-training models have been
proposed, applying such models directly might still be sub-optimal if more
labeled, training data could be obtained without a large cost. In this paper,
we present a weakly supervised framework for constructing ASR systems with
massive video data. As videos often contain human-speech audios aligned with
subtitles, we consider videos as an important knowledge source, and propose an
effective approach to extract high-quality audios aligned with transcripts from
videos based on Optical Character Recognition (OCR). The underlying ASR model
can be fine-tuned to fit any domain-specific target training datasets after
weakly supervised pre-training. Extensive experiments show that our framework
can easily produce state-of-the-art results on six public datasets for Mandarin
speech recognition.
