The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Recent Advances in End-to-End Automatic Speech Recognition
Open accessAbstract
Recently, the speech community is seeing a significant trend of moving from deep neural network based hybrid modeling to end-to-end (E2E) modeling for automatic speech recognition (ASR). While E2E models achieve the state-of-the-art results in most benchmarks in terms of ASR accuracy, hybrid models are still used in a large proportion of commercial ASR systems at the current time. There are lots of practical factors that affect the production model deployment decision. Traditional hybrid models, being optimized for production for decades, are usually good at these factors. Without providing excellent solutions to all these factors, it is hard for E2E models to be widely commercialized. In this paper, we will overview the recent advances in E2E models, focusing on technologies addressing those challenges from the industry’s perspective.
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Cited by
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Chenais, G., Lagarde, E., & Gil-Jardiné, C. (2023) · Journal of Medical Internet Research · doi:10.2196/40031 · Cited by 142
He, K., Mao, R., Lin, Q., Ruan, Y., Lan, X., Feng, M., & Cambria, E. (2024) · doi:10.2139/ssrn.4809363 · Cited by 69
Measuring the Accuracy of Automatic Speech Recognition Solutions
Kuhn, K., Kersken, V., Reuter, B., Egger, N., & Zimmermann, G. (2023) · ACM Transactions on Accessible Computing · doi:10.1145/3636513 · Cited by 44
“However, accuracy is not the only relevant factor for ASR, and E2E-models have shown weaknesses in streaming capabilities, latency and computational efficiency [Li 2022].”
Background
Metadata
- Authors
- Li, J.
- Venue
- APSIPA Transactions on Signal and Information Processing
- Publisher
- Emerald
- Published
- 2022-04-20
- Volume
- 11
- Issue
- 1
- Pages
- 1-64
- ISSN
- 2048-7703 (ISSN-L)
- OpenAlex
- W3211278025
- Cited by
- 395
- References
- 16
- FWCI
- 30.29
- Type
- journal article
- Language
- en
- Open access
- gold
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