# Dialog act guided contextual adapter for personalized speech recognition

arXiv: https://arxiv.org/abs/2303.17799 · HTML: https://mlsanity.com/paper/2303.17799/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [Contextual RNN-T For Open Domain ASR](https://mlsanity.com/paper/2006.03411/) (2020)
- [Contextual Adapters for Personalized Speech Recognition in Neural Transducers](https://mlsanity.com/paper/2205.13660/) (2022)
- [Context-Aware Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2111.03250/) (2021)
- [Personalization Strategies for End-to-End Speech Recognition Systems](https://mlsanity.com/paper/2102.07739/) (2021)
- [Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion](https://mlsanity.com/paper/1906.11604/) (2019)
- [No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models](https://mlsanity.com/paper/1712.01864/) (2017)
- [Domain-aware Neural Language Models for Speech Recognition](https://mlsanity.com/paper/2101.03229/) (2021)
- [Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model](https://mlsanity.com/paper/2005.07394/) (2020)
- [Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2112.06743/) (2021)
- [An Efficient Approach to Encoding Context for Spoken Language Understanding](https://mlsanity.com/paper/1807.00267/) (2018)

## Built on by (most cited first, 4 of 4)

- [CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition](https://mlsanity.com/paper/2502.18913/) (2025)
- [Promptformer: Prompted Conformer Transducer for ASR](https://mlsanity.com/paper/2401.07360/) (2024)
- [Locality enhanced dynamic biasing and sampling strategies for contextual ASR](https://mlsanity.com/paper/2401.13146/) (2024)
- [What Do Humans Hear When Interacting? Experiments on Selective Listening for Evaluating ASR of Spoken Dialogue Systems](https://mlsanity.com/paper/2508.04402/) (2025)

## Abstract (arXiv)

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog act, which is available in a multi-turn dialog scenario. In this work, we propose a dialog act guided contextual adapter network. Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing. On industrial voice assistant datasets, our model outperforms both the baselines - dialog act encoder-only model, and the contextual adaptation, leading to the most improvement over the no-context model: 58% average relative word error rate reduction (WERR) in the multi-turn dialog scenario, in comparison to the prior-art contextual adapter, which has achieved 39% WERR over the no-context model.