The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Experiments on neural net recognition of spoken and written text
Abstract
The problems are discussed of the recognition of handprinted and spoken digits and the handprinted and spoken English alphabet. Four such experiments were conducted and the results were compared to a conventional nearest-neighbor classifier trained on the same data. Results indicate that neural networks and nearest-neighbor classifiers perform at near the same level of accuracy. For each task, a critical number of neurons can be determined experimentally which yields highest recognition accuracy with least hardware. This number can also measure the classification efficiency of the input feature encoder. Several techniques for optimizing the performance of layered networks are discussed. A constant level added to the input signal biases patterns into the range where the learning rate is highest. Eliminating near-zero weights after learning results in little loss of accuracy. Finally, a novel handwriting encoder is described.>
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
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
An introduction to computing with neural nets
Lippmann, R. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,825
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,520
indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,923
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
“Neural” computation of decisions in optimization problems
Hopfield, J. J., & Tank, D. W. (1985) · Biological Cybernetics · doi:10.1007/bf00339943 · Cited by 6,031
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Pattern Classification and Scene Analysis.
Clarke, M. R. B., Duda, R. O., & Hart, P. E. (1974) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344977 · Cited by 3,222
Cited by
Multilayer perceptron, fuzzy sets, and classification
Pal, S. K., & Mitra, S. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.159058 · Cited by 1,113
Review of Neural Networks for Speech Recognition
Lippmann, R. P. (1989) · Neural Computation · doi:10.1162/neco.1989.1.1.1 · Cited by 477
Backpropagation neural nets with one and two hidden layers
de Villiers, J., & Barnard, E. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.182704 · Cited by 344
An iterative pruning algorithm for feedforward neural networks
Castellano, G., Fanelli, A. M., & Pelillo, M. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.572092 · Cited by 295
Metadata
- Authors
- Burr, D. J.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1988-07
- Volume
- 36
- Issue
- 7
- Pages
- 1162-1168
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2149272334
- Cited by
- 263
- References
- 17
- FWCI
- 31.48
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Experiments on neural net recognition of spoken and written text. https://app.cochat.ai/papers/10.1109/29.1643
- The paper
- Burr, D. J. (1988). Experiments on neural net recognition of spoken and written text. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/29.1643
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.
