The DOI matches its Crossref record.
A tree-based statistical language model for natural language speech recognition
Abstract
The problem of predicting the next word a speaker will say, given the words already spoken; is discussed. Specifically, the problem is to estimate the probability that a given word will be the next word uttered. Algorithms are presented for automatically constructing a binary decision tree designed to estimate these probabilities. At each node of the tree there is a yes/no question relating to the words already spoken, and at each leaf there is a probability distribution over the allowable vocabulary. Ideally, these nodal questions can take the form of arbitrarily complex Boolean expressions, but computationally cheaper alternatives are also discussed. Some results obtained on a 5000-word vocabulary with a tree designed to predict the next word spoken from the preceding 20 words are included. The tree is compared to an equivalent trigram model and shown to be superior.>
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
(2001) · Cited by 683
Statistical decision-tree models for parsing
Magerman, D. M. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981695 · Cited by 567
Tree Induction for Probability-Based Ranking
Provost, F., & Domingos, P. (2003) · Machine Learning · doi:10.1023/a:1024099825458 · Cited by 541
Automatic Speech Recognition: A Survey
Malik, M., Malik, M. K., Mehmood, K., & Makhdoom, I. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10073-7 · Cited by 358
Metadata
- Authors
- Bahl, L. R., Brown, P. F., de Souza, P. V., & Mercer, R. L.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1989-07
- Volume
- 37
- Issue
- 7
- Pages
- 1001-1008
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2099345940
- Cited by
- 255
- References
- 0
- FWCI
- 6.35
- Type
- journal article
- 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
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.
