mlsanityData to 7 Oct 2026

Speak & Spell: LLM-Driven Controllable Phonetic Error Augmentation for Robust Dialogue State Tracking

arXiv 2409.06263 ↗First posted 10 Sep 2024cs.CL cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Speech Recognition (ASR) systems. We introduce a simple yet effective data augmentation method that targets those entities to improve the robustness of DST model. Our novel method can control the placement of errors using keyword-highlighted prompts while introducing phonetically similar errors. As a result, our method generated sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

26 of 38 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  8. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
  9. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 800
  10. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  11. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  12. 2020SLURP: A Spoken Language Understanding Resource Packagecited by 197
14 more
  1. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  2. 2020TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Trackingcited by 130
  3. 2019Efficient Dialogue State Tracking by Selectively Overwriting Memorycited by 76
  4. 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
  5. 2021SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speechcited by 70
  6. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  7. 2019Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Modelcited by 52
  8. 2023LLM-powered Data Augmentation for Enhanced Cross-lingual Performancecited by 51
  9. 2021"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversationscited by 30
  10. 2023PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillationcited by 25
  11. 2019Learning ASR-Robust Contextualized Embeddings for Spoken Language Understandingcited by 20
  12. 2024Controlled Text Generation for Large Language Model with Dynamic Attribute Graphscited by 16
  13. 2020Improving Low Resource Code-switched ASR using Augmented Code-switched TTScited by 15
  14. 2023Adapting Text-based Dialogue State Tracker for Spoken Dialoguescited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Game-Oriented ASR Error Correction via RAG-Enhanced LLMcited by 0