# Dialog-context aware end-to-end speech recognition

arXiv: https://arxiv.org/abs/1808.02171 · HTML: https://mlsanity.com/paper/1808.02171/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 27 of 27)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation](https://mlsanity.com/paper/2502.11903/) (2025)
- [Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers](https://mlsanity.com/paper/2104.09426/) (2021)
- [Retrieval Augmented End-to-End Spoken Dialog Models](https://mlsanity.com/paper/2402.01828/) (2024)
- [Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation](https://mlsanity.com/paper/2102.07935/) (2021)
- [Updated Corpora and Benchmarks for Long-Form Speech Recognition](https://mlsanity.com/paper/2309.15013/) (2023)
- [Conversational Speech Recognition By Learning Conversation-level Characteristics](https://mlsanity.com/paper/2202.07855/) (2022)
- [Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR](https://mlsanity.com/paper/2207.01039/) (2022)
- [LongFNT: Long-form Speech Recognition with Factorized Neural Transducer](https://mlsanity.com/paper/2211.09412/) (2022)
- [Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems](https://mlsanity.com/paper/2306.13307/) (2023)
- [Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2112.06743/) (2021)
- [CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals](https://mlsanity.com/paper/2203.08774/) (2022)
- [Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition](https://mlsanity.com/paper/2301.02736/) (2023)
- [Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents](https://mlsanity.com/paper/2103.10325/) (2021)
- [BERT Attends the Conversation: Improving Low-Resource Conversational ASR](https://mlsanity.com/paper/2110.02267/) (2021)
- [Improving Transformer-based Conversational ASR by Inter-Sentential Attention Mechanism](https://mlsanity.com/paper/2207.00883/) (2022)
- [BASS: Block-wise Adaptation for Speech Summarization](https://mlsanity.com/paper/2307.08217/) (2023)
- [Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation](https://mlsanity.com/paper/2310.14278/) (2023)
- [Improved Long-Form Speech Recognition by Jointly Modeling the Primary and Non-primary Speakers](https://mlsanity.com/paper/2312.11123/) (2023)
- [Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASR](https://mlsanity.com/paper/2409.08797/) (2024)
- [Distilling Conversations: Abstract Compression of Conversational Audio Context for LLM-based ASR](https://mlsanity.com/paper/2603.26246/) (2026)
- [Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents](https://mlsanity.com/paper/2102.08147/) (2021)
- [Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2401.02417/) (2024)
- [Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview](https://mlsanity.com/paper/2403.00370/) (2024)
- [An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems](https://mlsanity.com/paper/2409.10515/) (2024)
- [BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR](https://mlsanity.com/paper/2609.36913/) (2026)

## Abstract (arXiv)

Existing speech recognition systems are typically built at the sentence level, although it is known that dialog context, e.g. higher-level knowledge that spans across sentences or speakers, can help the processing of long conversations. The recent progress in end-to-end speech recognition systems promises to integrate all available information (e.g. acoustic, language resources) into a single model, which is then jointly optimized. It seems natural that such dialog context information should thus also be integrated into the end-to-end models to improve further recognition accuracy. In this work, we present a dialog-context aware speech recognition model, which explicitly uses context information beyond sentence-level information, in an end-to-end fashion. Our dialog-context model captures a history of sentence-level context so that the whole system can be trained with dialog-context information in an end-to-end manner. We evaluate our proposed approach on the Switchboard conversational speech corpus and show that our system outperforms a comparable sentence-level end-to-end speech recognition system.