mlsanityData to 7 Oct 2026

Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities

arXiv 2304.13149 ↗First posted 25 Apr 2023cs.IR cs.CLcited by 4

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Abstract (arXiv)

Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. We discuss how query domain classification, knowledge graphs and user interaction data, and query personalization can be helpful to improve the accurate recognition of spoken information domain queries. Finally, we also provide a brief overview of current problems and challenges in speech recognition.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

25 of 43 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2023GPT-4 Technical Reportcited by 18,132
  5. 2019Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modelingcited by 71
  6. 2019Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entitiescited by 41
  7. 2022RescoreBERT: Discriminative Speech Recognition Rescoring with BERTcited by 34
  8. 2021Personalization Strategies for End-to-End Speech Recognition Systemscited by 26
  9. 2019An Investigation Into On-device Personalization of End-to-end Automatic Speech Recognition Modelscited by 25
  10. 2021Innovative Bert-based Reranking Language Models for Speech Recognitioncited by 23
  11. 2020Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducercited by 22
  12. 2022Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devicescited by 16
13 more
  1. 2019Latent Relation Language Modelscited by 14
  2. 2019Scalable Multi Corpora Neural Language Models for ASRcited by 12
  3. 2018Scalable language model adaptation for spoken dialogue systemscited by 11
  4. 2019An Empirical Study of Efficient ASR Rescoring with Transformerscited by 11
  5. 2019Connecting and Comparing Language Model Interpolation Techniquescited by 10
  6. 2020Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistantscited by 8
  7. 2021Error-driven Pruning of Language Models for Virtual Assistantscited by 8
  8. 2021Language model fusion for streaming end to end speech recognitioncited by 7
  9. 2021A Discriminative Entity-Aware Language Model for Virtual Assistantscited by 7
  10. 2022Space-Efficient Representation of Entity-centric Query Language Modelscited by 7
  11. 2019On Modeling ASR Word Confidencecited by 6
  12. 2022UserLibri: A Dataset for ASR Personalization Using Only Textcited by 6
  13. 2022ASR-Generated Text for Language Model Pre-training Applied to Speech Taskscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Transformer-based Model for ASR N-Best Rescoring and Rewritingcited by 3
  2. 2024FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech Datacited by 3
  3. 2026SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noisecited by 1
  4. 2025Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correctioncited by 0