Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition
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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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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.
“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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Niu, H.-W., van Leeuwen, C. V., Hao, J., Wang, G.-X., & Lachmann, T. (2022) · Applied Sciences · doi:10.3390/app12136510 · Cited by 36 · see citing works
Hierarchical Multitask Learning With CTC
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End-to-end Contextual Speech Recognition Using Class Language Models and a Token Passing Decoder
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Advancing Connectionist Temporal Classification with Attention Modeling
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Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition
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Self-citationVisual features for context-aware speech recognition
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- 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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