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Transformer and recurrent neural-network transducer models containing up to one billion parameters have produced a marked decrease in word error rate for automated transcription of spoken English-language sentences.
“In particular, transformer1 and recurrent neural-network transducer (RNNT)12,13,16 models containing up to one billion parameters2 have produced a marked decrease in word error rate (WER) (and therefore much better accuracy) for the automated transcription of spoken English-language sentences, as shown in Fig. 1a for two widely used datasets, Librispeech17 and SwitchBoard18.”
From An analog-AI chip for energy-efficient speech recognition and transcription (2023) · cites this paper as [12]
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A New Representation of Skeleton Sequences for 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793
An analog-AI chip for energy-efficient speech recognition and transcription
Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06337-5 · Cited by 269
Automatic speech recognition of conversational speech in individuals with disordered speech
Tobin, J., Nelson, P., MacDonald, B., Heywood, R., Cave, R., Seaver, K., Desjardins, A., Jiang, P.-P., & Green, J. R. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2024_jslhr-24-00045 · Cited by 45
Metadata
- Authors
- Graves, A.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Graves, Alex
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 15-35
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W4249179470
- Cited by
- 265
- References
- 0
- Citations by year
- 2026: 8 · 2025: 26 · 2024: 25 · 2023: 48 · 2022: 29 · 2021: 29 · 2020: 41 · 2019: 13 · 2018: 14 · 2017: 25 · 2016: 5 · 2014: 1 · 2013: 1
- FWCI
- 1.13
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in computational intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- artificial neural networks, multilayer perceptron, pattern classification, recurrent neural networks, sequence labeling, contextual information, generalization, network training
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