An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model
Open accessAbstract
Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with an neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.
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References 22 · Cited by 253
- Authors
- Kannan, A., Wu, Y., Nguyen, P., Sainath, T. N., Chen, Z., & Prabhavalkar, R.
- Affiliations
- Google (United States) (US)
- Venue
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 1-5828
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2775766866
- Cited by
- 253
- References
- 22
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 25 · 2023: 48 · 2022: 32 · 2021: 39 · 2020: 35 · 2019: 27 · 2018: 17
- FWCI
- 12.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- shallow fusion, attention-based sequence-to-sequence models, speech recognition, neural language models, beam search decoding, word error rate reduction
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