The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding
Open accessAbstract
The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically simplifies the existing pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen involves learning a single recurrent neural network (RNN) predicting context-independent targets (phonemes or characters). To remove the need for pre-generated frame labels, we adopt the connectionist temporal classification (CTC) objective function to infer the alignments between speech and label sequences. A distinctive feature of Eesen is a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that compared with the standard hybrid DNN systems, Eesen achieves comparable word error rates (WERs), while at the same time speeding up decoding significantly.
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Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
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Cited by
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
Chan, W., Jaitly, N., Le, Q., & Vinyals, O. (2016) · 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2016.7472621 · Cited by 2,226
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Amodei, D., Anubhai, R., Battenberg, E., Case, C. T., Casper, J., Catanzaro, B., Chen, J., Chrzanowski, M., Coates, A., Diamos, G., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.02595 · Cited by 2,110
Streaming End-to-end Speech Recognition for Mobile Devices
He, Y., Sainath, T. N., Prabhavalkar, R., McGraw, I., Alvarez, R., Zhao, D., Rybach, D., Kannan, A., Wu, Y., Pang, R., et al. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682336 · Cited by 601
A high-performance speech neuroprosthesis
Willett, F. R., Kunz, E. M., Fan, C., Avansino, D. T., Wilson, G. H., Choi, E. Y., Kamdar, F., Glasser, M. F., Hochberg, L. R., Druckmann, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06377-x · Cited by 581
Towards Better Decoding and Language Model Integration in Sequence to Sequence Models
Chorowski, J., & Jaitly, N. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-343 · Cited by 358
Metadata
- Authors
- Miao, Y., Gowayyed, M., & Metze, F.
- Venue
- 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 167-174
- OpenAlex
- W2963211739
- Cited by
- 707
- References
- 8
- FWCI
- 44.51
- Type
- conference paper
- Language
- en
- Open access
- green
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- Miao, Y., Gowayyed, M., & Metze, F. (2015). EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). https://doi.org/10.1109/asru.2015.7404790
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