Large scale hierarchical neural network language models
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Hierarchical NNLM can reduce the training time of feedforward networks considerably because different n-grams are used to train each word prediction model.
“Hierarchical NNLM is a class-based model that consists of a neural network that predicts the class and separate neural networks that predict the word inside a class [36]. This can reduce training time of feedforward networks considerably, because different n-grams are used to train each word prediction model.”
From Automatic Speech Recognition With Very Large Conversational Finnish and Estonian Vocabularies (2017) · cites this paper as [36]
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Automatic Speech Recognition With Very Large Conversational Finnish and Estonian Vocabularies
Enarvi, S., Smit, P., Virpioja, S., & Kurimo, M. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2743344 · Cited by 33 · see citing works
- Authors
- Kuo, H.-K., Arısoy, E., Emami, A., & Vozila, P.
- Venue
- Interspeech 2012
- Publisher
- ISCA
- Published
- 2012-09-09
- Pages
- 1672-1675
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2396033037
- Cited by
- 26
- References
- 0
- Citations by year
- 2020: 1 · 2019: 1 · 2018: 2 · 2017: 3 · 2016: 3 · 2015: 5 · 2014: 3 · 2013: 5 · 2012: 3
- FWCI
- 3.93
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- hierarchical neural networks, language models, large-scale neural networks
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.
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