Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly Supervised Construction of ASR Systems with Massive Video Data   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.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol