# Conformer-based Hybrid ASR System for Switchboard Dataset

arXiv: https://arxiv.org/abs/2111.03442 · HTML: https://mlsanity.com/paper/2111.03442/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [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)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://mlsanity.com/paper/1910.09799/) (2019)
- [RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation](https://mlsanity.com/paper/1905.03072/) (2019)
- [Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition](https://mlsanity.com/paper/2109.01163/) (2021)
- [Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard](https://mlsanity.com/paper/2001.07263/) (2020)
- [RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition](https://mlsanity.com/paper/1805.05225/) (2018)
- [On the limit of English conversational speech recognition](https://mlsanity.com/paper/2105.00982/) (2021)
- [Self-Attentional Acoustic Models](https://mlsanity.com/paper/1803.09519/) (2018)
- [Deja-vu: Double Feature Presentation and Iterated Loss in Deep Transformer Networks](https://mlsanity.com/paper/1910.10324/) (2019)
- [Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/2010.16368/) (2020)
- [The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment](https://mlsanity.com/paper/2004.00960/) (2020)
- [Cumulative Adaptation for BLSTM Acoustic Models](https://mlsanity.com/paper/1906.06207/) (2019)
- [LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring](https://mlsanity.com/paper/1907.01030/) (2019)
- [A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition](https://mlsanity.com/paper/1606.06871/) (2016)
- [Efficient Conformer with Prob-Sparse Attention Mechanism for End-to-EndSpeech Recognition](https://mlsanity.com/paper/2106.09236/) (2021)

## Built on by (most cited first, 15 of 15)

- [Efficient Training of Neural Transducer for Speech Recognition](https://mlsanity.com/paper/2204.10586/) (2022)
- [Improving the Training Recipe for a Robust Conformer-based Hybrid Model](https://mlsanity.com/paper/2206.12955/) (2022)
- [Serialized Output Training by Learned Dominance](https://mlsanity.com/paper/2407.03966/) (2024)
- [Comparative Analysis of the wav2vec 2.0 Feature Extractor](https://mlsanity.com/paper/2308.04286/) (2023)
- [Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech](https://mlsanity.com/paper/2210.13397/) (2022)
- [Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition](https://mlsanity.com/paper/2306.11309/) (2023)
- [Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation](https://mlsanity.com/paper/2306.15171/) (2023)
- [Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering](https://mlsanity.com/paper/2506.03681/) (2025)
- [How to Evaluate Speech Translation with Source-Aware Neural MT Metrics](https://mlsanity.com/paper/2511.03295/) (2025)
- [Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project](https://mlsanity.com/paper/2309.15869/) (2023)
- [Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous Clients](https://mlsanity.com/paper/2405.17376/) (2024)
- [Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition](https://mlsanity.com/paper/2603.04945/) (2026)
- [Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems](https://mlsanity.com/paper/2306.14608/) (2023)
- [Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring](https://mlsanity.com/paper/2503.09661/) (2025)
- [SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies](https://mlsanity.com/paper/2607.25716/) (2026)

## Abstract (arXiv)

The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets. To our best knowledge, the impact of using conformer acoustic model for hybrid ASR is not investigated. In this paper, we present and evaluate a competitive conformer-based hybrid model training recipe. We study different training aspects and methods to improve word-error-rate as well as to increase training speed. We apply time downsampling methods for efficient training and use transposed convolutions to upsample the output sequence again. We conduct experiments on Switchboard 300h dataset and our conformer-based hybrid model achieves competitive results compared to other architectures. It generalizes very well on Hub5'01 test set and outperforms the BLSTM-based hybrid model significantly.