The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
From Feedforward to Recurrent LSTM Neural Networks for Language Modeling
Abstract
Language models have traditionally been estimated based on relative frequencies, using count statistics that can be extracted from huge amounts of text data. More recently, it has been found that neural networks are particularly powerful at estimating probability distributions over word sequences, giving substantial improvements over state-of-the-art count models. However, the performance of neural network language models strongly depends on their architectural structure. This paper compares count models to feedforward, recurrent, and long short-term memory (LSTM) neural network variants on two large-vocabulary speech recognition tasks. We evaluate the models in terms of perplexity and word error rate, experimentally validating the strong correlation of the two quantities, which we find to hold regardless of the underlying type of the language model. Furthermore, neural networks incur an increased computational complexity compared to count models, and they differently model context dependences, often exceeding the number of words that are taken into account by count based approaches. These differences require efficient search methods for neural networks, and we analyze the potential improvements that can be obtained when applying advanced algorithms to the rescoring of word lattices on large-scale setups.
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Financial time series forecasting model based on CEEMDAN and LSTM
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“A neural approach to automated essay scoring,”
Taghipour, K., & Ng, H. T. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1193 · Cited by 537
The Microsoft 2017 Conversational Speech Recognition System
Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461870 · Cited by 472
Toward Human Parity in Conversational Speech Recognition
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M. L., Stolcke, A., Yu, D., & Zweig, G. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2756440 · Cited by 245
Metadata
- Authors
- Sundermeyer, M., Ney, H., & Schluter, R.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2015-03
- Volume
- 23
- Issue
- 3
- Pages
- 517-529
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W1985258458
- Cited by
- 507
- References
- 43
- FWCI
- 38.79
- Type
- journal article
- Language
- en
- Open access
- closed
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