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The design for the wall street journal-based CSR corpus
Open accessAbstract
The DARPA Spoken Language System (SLS) community has long taken a leadership position in designing, implementing, and globally distributing significant speech corpora widely used for advancing speech recognition research. The Wall Street Journal (WSJ) CSR Corpus described here is the newest addition to this valuable set of resources. In contrast to previous corpora, the WSJ corpus will provide DARPA its first general-purpose English, large vocabulary, natural language, high perplexity, corpus containing significant quantities of both speech data (400 hrs.) and text data (47M words), thereby providing a means to integrate speech recognition and natural language processing in application domains with high potential practical value. This paper presents the motivating goals, acoustic data design, text processing steps, lexicons, and testing paradigms incorporated into the multi-faceted WSJ CSR Corpus.
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Cited by
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Anumanchipalli, G. K., Chartier, J., & Chang, E. F. (2019) · Nature · doi:10.1038/s41586-019-1119-1 · Cited by 883
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Zhang, M., & Sawchuk, A. A. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370438 · Cited by 554
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.044 · Cited by 544
Metadata
- Authors
- Paul, D. B., & Baker, J. M.
- Venue
- Proceedings of the workshop on Speech and Natural Language - HLT '91
- Published
- 1992
- Pages
- 357
- OpenAlex
- W2024490156
- Cited by
- 1,099
- References
- 0
- FWCI
- 14.25
- Type
- conference paper
- Language
- en
- Open access
- bronze
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