# Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion

arXiv: https://arxiv.org/abs/1906.11604 · HTML: https://mlsanity.com/paper/1906.11604/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [Transformer Language Models with LSTM-based Cross-utterance Information Representation](https://mlsanity.com/paper/2102.06474/) (2021)
- [Adapting Long Context NLM for ASR Rescoring in Conversational Agents](https://mlsanity.com/paper/2104.11070/) (2021)
- [Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding](https://mlsanity.com/paper/2204.05169/) (2022)
- [Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition](https://mlsanity.com/paper/2106.06922/) (2021)
- [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)
- [Dialog act guided contextual adapter for personalized speech recognition](https://mlsanity.com/paper/2303.17799/) (2023)
- [Improved Long-Form Speech Recognition by Jointly Modeling the Primary and Non-primary Speakers](https://mlsanity.com/paper/2312.11123/) (2023)
- [Distilling Conversations: Abstract Compression of Conversational Audio Context for LLM-based ASR](https://mlsanity.com/paper/2603.26246/) (2026)
- [VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition](https://mlsanity.com/paper/2305.19972/) (2023)
- [Synergistic pathways of modulation enable robust task packing within neural dynamics](https://mlsanity.com/paper/2408.01316/) (2024)
- [An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems](https://mlsanity.com/paper/2409.10515/) (2024)

## Abstract (arXiv)

We present a novel conversational-context aware end-to-end speech recognizer based on a gated neural network that incorporates conversational-context/word/speech embeddings. Unlike conventional speech recognition models, our model learns longer conversational-context information that spans across sentences and is consequently better at recognizing long conversations. Specifically, we propose to use the text-based external word and/or sentence embeddings (i.e., fastText, BERT) within an end-to-end framework, yielding a significant improvement in word error rate with better conversational-context representation. We evaluated the models on the Switchboard conversational speech corpus and show that our model outperforms standard end-to-end speech recognition models.