The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
Open accessAbstract
We present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. We model the output vocabulary of about 100,000 words directly using deep bi-directional LSTM RNNs with CTC loss. The model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. We show that the CTC word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. We demonstrate that the CTC word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
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“Additionally, Soltau et al. (2017) have proved that a simple end-to-end model can produce better results when trained with large-scale data, compared with a complex model trained with small-scale data.”
From MOPRD: A multidisciplinary open peer review dataset (2022) · cites this paper as Soltau et al. (2017)
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,202
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973
Self-citationDeep Speech 2: End-to-End Speech Recognition in English and MandarinPreprint
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 2,265
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
Towards End-To-End Speech Recognition with Recurrent Neural Networks
Graves, A., & Jaitly, N. (2014) · Cited by 1,861
End-to-end attention-based large vocabulary speech recognition
Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., & Bengio, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472618 · Cited by 1,161
EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding
Miao, Y., Gowayyed, M. A., & Metze, F. (2015) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2015.7404790 · Cited by 727
Research works printed but not linked: 2
- L.R. Bahl, P.V. de Souza, P.S. Gopalakrishnan, D. Nahamoo, and M.A. Picheny. Context dependent modelling of phones in continuous speech using decision trees. In Proc. DARPA Speech and Natural Language Processing Workshop, 1991.
- Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks. In Proc. ICML, 2006.
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A Progressive Generation Framework with Speech Pre-trained Model for Expressive Voice Conversion
Wang, T., Ge, M., Niu, Z., Gong, C., Qiang, C., Wang, H., Huang, Z., Ma, Z., Wang, X., Chen, X., et al. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209157
A Conv-LSTM-SA Model Based on Hybrid Deep Learning for Daily Natural Gas Consumption Forecasting
Zhu, M., Cui, C., Ji, Y., & Zhu, C. (2025) · International Symposium on AI and Cybersecurity (ISAICS) · doi:10.1109/isaics66888.2025.11349995
Découverte d'unités linguistiques à l'aide de méthodes d'apprentissage non supervisé
Manenti, C. (2019) · theses.fr (ABES) · doi:10.70675/852c6e6bz6e10z4886za02bz4febc78e3a0e
Metadata
- Authors
- Soltau, H., Liao, H., & Sak, H.
- Affiliations
- Google (United States) (US)
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 3707-3711
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2545177271
- Cited by
- 304
- References
- 21
- Citations by year
- 2025: 9 · 2024: 6 · 2023: 26 · 2022: 25 · 2021: 37 · 2020: 39 · 2019: 73 · 2018: 58 · 2017: 31
- FWCI
- 25.07
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- public-domain
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- large vocabulary continuous speech recognition, bidirectional LSTM, CTC loss, data sparsity, pronunciation dictionary
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