# Attention Enhanced Citrinet for Speech Recognition

arXiv: https://arxiv.org/abs/2209.00261 · HTML: https://mlsanity.com/paper/2209.00261/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View](https://mlsanity.com/paper/1906.02762/) (2019)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019)
- [Jasper: An End-to-End Convolutional Neural Acoustic Model](https://mlsanity.com/paper/1904.03288/) (2019)
- [Very Deep Self-Attention Networks for End-to-End Speech Recognition](https://mlsanity.com/paper/1904.13377/) (2019)
- [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://mlsanity.com/paper/2104.01721/) (2021)
- [Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture](https://mlsanity.com/paper/2001.08290/) (2020)
- [Efficient conformer-based speech recognition with linear attention](https://mlsanity.com/paper/2104.06865/) (2021)

## Abstract (arXiv)

Citrinet is an end-to-end convolutional Connectionist Temporal Classification (CTC) based automatic speech recognition (ASR) model. To capture local and global contextual information, 1D time-channel separable convolutions combined with sub-word encoding and squeeze-and-excitation (SE) are used in Citrinet, making the whole architecture to be as deep as including 23 blocks with 235 convolution layers and 46 linear layers. This pure convolutional and deep architecture makes Critrinet relatively slow at convergence. In this paper, we propose to introduce multi-head attentions together with feed-forward networks in the convolution module in Citrinet blocks while keeping the SE module and residual module unchanged. For speeding up, we remove 8 convolution layers in each attention-enhanced Citrinet block and reduce 23 blocks to 13. Experiments on the Japanese CSJ-500h and Magic-1600h dataset show that the attention-enhanced Citrinet with less layers and blocks and converges faster with lower character error rates than (1) Citrinet with 80% training time and (2) Conformer with 40% training time and 29.8% model size.