# On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition

arXiv: https://arxiv.org/abs/2005.14327 · HTML: https://mlsanity.com/paper/2005.14327/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 30 of 63)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://mlsanity.com/paper/2101.07597/) (2021)
- [Scaling End-to-End Models for Large-Scale Multilingual ASR](https://mlsanity.com/paper/2104.14830/) (2021)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition](https://mlsanity.com/paper/2102.01380/) (2021)
- [Tied & Reduced RNN-T Decoder](https://mlsanity.com/paper/2109.07513/) (2021)
- [Transformer Based Deliberation for Two-Pass Speech Recognition](https://mlsanity.com/paper/2101.11577/) (2021)
- [A Configurable Multilingual Model is All You Need to Recognize All Languages](https://mlsanity.com/paper/2107.05876/) (2021)
- [4-bit Conformer with Native Quantization Aware Training for Speech Recognition](https://mlsanity.com/paper/2203.15952/) (2022)
- [JOIST: A Joint Speech and Text Streaming Model For ASR](https://mlsanity.com/paper/2210.07353/) (2022)
- [On Addressing Practical Challenges for RNN-Transducer](https://mlsanity.com/paper/2105.00858/) (2021)
- [Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition](https://mlsanity.com/paper/2106.02302/) (2021)
- [Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition](https://mlsanity.com/paper/2110.05354/) (2021)
- [Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition](https://mlsanity.com/paper/2112.05820/) (2021)
- [Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems](https://mlsanity.com/paper/2112.05842/) (2021)
- [Turn-Taking Prediction for Natural Conversational Speech](https://mlsanity.com/paper/2208.13321/) (2022)
- [Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition](https://mlsanity.com/paper/2103.00422/) (2021)
- [A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability](https://mlsanity.com/paper/2211.02499/) (2022)
- [XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition](https://mlsanity.com/paper/2103.08207/) (2021)
- [A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes](https://mlsanity.com/paper/2204.06164/) (2022)
- [Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models](https://mlsanity.com/paper/2212.01992/) (2022)
- [Conformer with dual-mode chunked attention for joint online and offline ASR](https://mlsanity.com/paper/2206.11157/) (2022)
- [LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers](https://mlsanity.com/paper/2211.02809/) (2022)
- [Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR](https://mlsanity.com/paper/2304.04974/) (2023)
- [A Language Agnostic Multilingual Streaming On-Device ASR System](https://mlsanity.com/paper/2208.13916/) (2022)
- [An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2106.09885/) (2021)
- [Federated Pruning: Improving Neural Network Efficiency with Federated Learning](https://mlsanity.com/paper/2209.06359/) (2022)
- [Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2309.15796/) (2023)
- [Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition](https://mlsanity.com/paper/2110.04891/) (2021)

## Abstract (arXiv)

Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E methods: recurrent neural network transducer (RNN-T), RNN attention-based encoder-decoder (AED), and Transformer-AED. In this study, we conduct an empirical comparison of RNN-T, RNN-AED, and Transformer-AED models, in both non-streaming and streaming modes. We use 65 thousand hours of Microsoft anonymized training data to train these models. As E2E models are more data hungry, it is better to compare their effectiveness with large amount of training data. To the best of our knowledge, no such comprehensive study has been conducted yet. We show that although AED models are stronger than RNN-T in the non-streaming mode, RNN-T is very competitive in streaming mode if its encoder can be properly initialized. Among all three E2E models, transformer-AED achieved the best accuracy in both streaming and non-streaming mode. We show that both streaming RNN-T and transformer-AED models can obtain better accuracy than a highly-optimized hybrid model.