# End-to-End Speech Recognition: A Survey

**Authors:** Rohit Prabhavalkar, Takaaki Hori, Tara N. Sainath, Ralf Schlüter, Shinji Watanabe  
**arXiv:** [2303.03329](https://arxiv.org/abs/2303.03329)  
**Date:** 2023-03-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.03329v1/

## Key points

- Research in Automatic Speech Recognition (ASR) has made significant advancements with the introduction of Deep Learning techniques.
- All-neural ASR architectures, known as End-to-End (E2E) models, have emerged as the prominent approach in ASR.
- E2E models have led to a remarkable reduction in word error rate by more than 50% compared to traditional modeling approaches without Deep Learning.
- The survey provides a comprehensive taxonomy of E2E ASR models and discusses their improvements.
- It explores the relationship between E2E models and the classical Hidden Markov Model (HMM)-based ASR architecture.
- The survey covers various aspects of E2E ASR, including modeling, training, decoding, and integration with external language models.
- It delves into performance evaluation and deployment opportunities for E2E ASR models while offering insights into potential future developments in this field.
- Commercial deployment of E2E ASR architectures is still limited despite their dominance in academic discussions.
- Areas for future work are highlighted to bridge the gap between academic research and commercial implementation.
- Challenges need to be addressed before E2E models can become widely adopted commercially.

## Summary

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In the past decade, research in Automatic Speech Recognition (ASR) has made significant advancements with the introduction of Deep Learning techniques. These advancements have led to a remarkable reduction in word error rate by more than 50% compared to traditional modeling approaches without Deep Learning. As a result, all-neural ASR architectures, known as End-to-End (E2E) models, have emerged as the prominent approach in ASR. E2E models represent a highly integrated and completely neural approach to ASR, relying heavily on general machine learning knowledge and consistently learning from data. The main objective of this survey is to provide a comprehensive taxonomy of E2E ASR models and discuss their improvements. It also explores the relationship between these models and the classical Hidden Markov Model (HMM)-based ASR architecture. The survey covers various aspects of E2E ASR, including modeling, training, decoding, and integration with external language models. Furthermore, it delves into performance evaluation and deployment opportunities for E2E ASR models while offering insights into potential future developments in this field. Despite their dominance in academic discussions, commercial deployment of E2E ASR architectures is still limited. The authors highlight areas for future work in order to bridge this gap between academic research and commercial implementation. While E2E models show great promise in improving accuracy and efficiency in ASR, there are challenges that need to be addressed before they can become widely adopted commercially. Overall,<kgd>Automatic Speech Recognition</kgd> has greatly benefited from the advancements in <kgd>Deep Learning</kgd>, leading to the emergence of <kgd>End-to-End Models</kgd> as the prominent approach in ASR. This survey provides a comprehensive overview of these models and their significance in advancing the field of automatic speech recognition, including their relationship with the traditional <kgd>Hidden Markov Model</kgd> architecture. It also discusses various aspects of E2E ASR, such as modeling, training, decoding, and integration with external language models, while offering insights into performance evaluation and potential future developments. However,<kgd>Evaluation and Deployment Opportunities</kgd> for E2E ASR models are still limited commercially, highlighting areas for future work to bridge the gap between academic research and commercial implementation. Despite challenges that need to be addressed,<kgd>End-to-End Models</kgd> show great promise in improving accuracy and efficiency in ASR, making them a significant advancement in this field.