The DOI matches its Crossref record.
A robust parser for spoken language understanding
Abstract
This paper describes a robust parsing algorithm for spoken language understanding. Comparing with the other work in robust parsing, we focus on building a parser that is robust to not only ill-formed spontaneous spoken language inputs but also under-specified grammars. Preliminary experiment results show that the parsing performance deteriorates more gracefully than another parser we have used when the grammar is more underspecified. 1.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory
Toda, T., Black, A. W., & Tokuda, K. (2007) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/tasl.2007.907344 · Cited by 937
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Veaux, C., Yamagishi, J., & King, S. (2013) · 2013 International Conference Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE) · doi:10.1109/icsda.2013.6709856 · Cited by 398
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., & Schroeder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.27 · Cited by 381
A survey on neural speech synthesis
Tan, X., Qin, T., Soong, F., & Liu, T.-Y. (2021) · arXiv · doi:10.48550/arxiv.2106.15561 · Cited by 216
Metadata
- Authors
- Wang, Y.-Y.
- Venue
- 6th European Conference on Speech Communication and Technology
- Publisher
- ISCA
- Published
- 1999-09-05
- Pages
- 2055-2058
- OpenAlex
- W1600722501
- Cited by
- 653
- References
- 0
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A robust parser for spoken language understanding. https://app.cochat.ai/papers/10.21437/eurospeech.1999-513
- The paper
- Wang, Y.-Y. (1999). A robust parser for spoken language understanding. 6th European Conference on Speech Communication and Technology. https://doi.org/10.21437/eurospeech.1999-513
Data sources
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.