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

speech-audio › speech-recognition

📄 Abstract

Abstract: 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.

Authors (4)

Jihyun Lee

Solee Im

Wonjun Lee

Gary Geunbae Lee

Submitted

September 10, 2024

arXiv Category

cs.CL

arXiv PDF

Key Contributions

Introduces 'Speak & Spell', an LLM-driven data augmentation method for improving Dialogue State Tracking (DST) robustness against Automatic Speech Recognition (ASR) errors. The method uses LLMs to control the placement of phonetically similar errors on keywords via prompts, generating sufficient error patterns to significantly improve DST accuracy in noisy ASR environments.

Business Value

Enhances the reliability and user experience of voice-based applications (e.g., virtual assistants, call center bots) by making them more resilient to speech recognition inaccuracies.

Paper Metadata

Innovation Type

Data Augmentation Technique

Deployment Feasibility

High, as it leverages existing LLMs and can be integrated into data augmentation pipelines.

Limitations Addressed

Reduced accuracy of DST in spoken dialogue due to ASR errors,Lack of effective methods to generate realistic phonetic errors,Need for DST models robust to low-accuracy ASR

Performance Gains

Improved accuracy in noised and low-accuracy ASR environments.

Technical Tags

Dialogue State Tracking (DST)Phonetic Error AugmentationLLM-Driven AugmentationRobustnessAutomatic Speech Recognition (ASR)Named Entity ErrorsData AugmentationTask-Oriented Dialogue SystemsNatural Language Understanding

Research Topics

Improving Robustness of Dialogue SystemsHandling ASR Errors in DialogueData Augmentation for NLPLLM Applications in Speech ProcessingDialogue State Tracking

Methods & Architectures

LLM-driven controllable phonetic error augmentationKeyword-highlighted promptsPhonetically similar error generation Dialogue State Trackers (DST)Large Language Models (LLMs)Automatic Speech Recognition (ASR) systems

Applications & Tasks

Speech Recognition Natural Language Understanding Dialogue Systems Human-Computer Interaction Degradation of DST accuracy due to ASR errorsDifficulty in generating realistic phonetic errorsNeed for robust DST models in noisy environments Dialogue State TrackingImproving ASR robustnessData augmentation for dialogue systems

Related Fields

Speech ProcessingNatural Language ProcessingMachine LearningDialogue Systems

Keywords

Dialogue State TrackingDSTPhonetic Error AugmentationLLM-DrivenRobustnessASR ErrorsData AugmentationTask-Oriented DialogueSpeech RecognitionNamed EntitiesControllable GenerationKeyword HighlightingNLPVoice Assistants

Academic Context

#Improving Robustness of Dialogue Systems#Handling ASR Errors in Dialogue#Data Augmentation for NLP#LLM Applications in Speech Processing#Dialogue State Tracking

Commercial Potential

Potential Products

Data augmentation tools for dialogue systemsRobust DST modules for voice applications

Target Industries

TechnologyTelecommunicationsCustomer ServiceAutomotive (In-car assistants)

Use Case Examples

Improving the accuracy of voice assistants in noisy environments (e.g., cars, public places)Making call center automation systems more reliableEnhancing the performance of in-car voice control systems

Competitive Edge

Offers a novel, LLM-powered approach to generate targeted phonetic errors for data augmentation, specifically addressing ASR-induced errors in DST.

Market Opportunity

Growing market for conversational AI and voice technology.

Revenue Models

Licensing of augmentation techniquesdevelopment of specialized DST components.

Resource Requirements

Compute Needs

Moderate, requires LLM inference for augmentation.

Data Requirements

Requires dialogue data and ASR output for evaluation.

Deployment Constraints

Effectiveness depends on the quality of the LLM used for augmentation and the specific ASR errors encountered.

Scalability

Scales with the LLM's generation capabilities and the size of the dialogue dataset.

Production Readiness

Maturity Level

Research/Development

Time to Market

6-12 months for integration into dialogue system development pipelines.

Patent Potential

Moderate, for the specific LLM-driven phonetic error augmentation technique.

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