The DOI matches its Crossref record.
Bootstrap estimates for confidence intervals in ASR performance evaluation
Abstract
The field of speech recognition has clearly benefited from precisely defined testing conditions and objective performance measures such as word error rate. In the development and evaluation of new methods, the question arises whether the empirically observed difference in performance is due to a genuine advantage of one system over the other, or just an effect of chance. However, many publications still do not concern themselves with the statistical significance of the results reported. We present a bootstrap method for significance analysis which is, at the same time, intuitive, precise and and easy to use. Unlike some methods, we make no (possibly ill-founded) approximations and the results are immediately interpretable in terms of word error rate.
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Citations
Cited by
Joint-sequence models for grapheme-to-phoneme conversion
Bisani, M., & Ney, H. (2008) · Speech Communication · doi:10.1016/j.specom.2008.01.002 · Cited by 599
Self-citation(2012) · Cited by 163
Metadata
- Authors
- Bisani, M., & Ney, H.
- Venue
- 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
- Publisher
- IEEE
- Published
- 2004-09-28
- Volume
- 1
- Pages
- I-409-12
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2107223151
- Cited by
- 227
- References
- 0
- FWCI
- 6.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bisani, M., & Ney, H. (2004). Bootstrap estimates for confidence intervals in ASR performance evaluation. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp.2004.1326009
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
