The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A cache-based natural language model for speech recognition
Open accessAbstract
Speech-recognition systems must often decide between competing ways of breaking up the acoustic input into strings of words. Since the possible strings may be acoustically similar, a language model is required; given a word string, the model returns its linguistic probability. Several Markov language models are discussed. A novel kind of language model which reflects short-term patterns of word use by means of a cache component (analogous to cache memory in hardware terminology) is presented. The model also contains a 3g-gram component of the traditional type. The combined model and a pure 3g-gram model were tested on samples drawn from the Lancaster-Oslo/Bergen (LOB) corpus of English text. The relative performance of the two models is examined, and suggestions for the future improvements are made.>
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
A unified architecture for natural language processing: deep neural networks with multitask learning
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An introduction to hidden Markov models
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
Very Deep Convolutional Networks for Text Classification
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Neural Machine Translation of Rare Words with Subword Units
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The unreasonable effectiveness of deep learning in artificial intelligence
Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907373117 · Cited by 352
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021068 · Cited by 346
Cited by
Composition in Distributional Models of Semantics
Mitchell, J., & Lapata, M. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01106.x · Cited by 960
Context dependent recurrent neural network language model
Mikolov, T., & Zweig, G. (2012) · 2012 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424228 · Cited by 560
A bit of progress in language modeling
Goodman, J. T. (2001) · Computer Speech & Language · doi:10.1006/csla.2001.0174 · Cited by 469
Adaptive Statistical Language Modeling: A Maximum Entropy Approach
Rosenfeld, R. (2018) · doi:10.1184/r1/6603032 · Cited by 269
Tu, Z., Su, Z., & Devanbu, P. (2014) · Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2635868.2635875 · Cited by 238
Metadata
- Authors
- Kuhn, R., & De Mori, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1990-06
- Volume
- 12
- Issue
- 6
- Pages
- 570-583
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2127836646
- Cited by
- 495
- References
- 8
- FWCI
- 8.31
- Type
- journal article
- Language
- en
- Open access
- green
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