Cited by 3,150Speech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing

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Conformer: Convolution-augmented Transformer for Speech Recognition

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Gulati, Anmol, Qin, James, Chiu, Chung-Cheng, Parmar, Niki, Zhang, Yu, Yu, Jiahui, Han, Wei, Wang, Shibo, Zhang, Zhengdong, Wu, Yonghui, Pang, Ruoming·2020·Interspeech 2020

DOI 10.21437/interspeech.2020-3015

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Abstract

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters.

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Authors: Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., & Pang, R.
Affiliations: Google (United States) (US)
ORCID: Gulati, Anmol
Chiu, Chung-Cheng
Zhang, Yu
Yu, Jiahui
Han, Wei
Wang, Shibo
Zhang, Zhengdong
Wu, Yonghui
Venue: Interspeech 2020
Publisher: ISCA
Published: 2020-10-25
Pages: 5036-5040
ISSN: 2308-457X (ISSN-L)
DOI: 10.21437/interspeech.2020-3015
OpenAlex: W3097777922
Cited by: 3,150
References: 33
Citations by year: 2026: 373 · 2025: 666 · 2024: 644 · 2023: 708 · 2022: 424 · 2021: 300 · 2020: 35
FWCI: 140.10
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)
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 › Signal Processing › Music and Audio Processing
Keywords: conformational isomers, speech recognition, CNN-Transformer hybrid, parameter-efficient models, LibriSpeech, word error rate

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Conformer: Convolution-augmented Transformer for Speech Recognition

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