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Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses

speech-audio › speech-recognition

📄 Abstract

Abstract: This paper introduces a new paradigm for generative error correction (GER) framework in audio-visual speech recognition (AVSR) that reasons over modality-specific evidences directly in the language space. Our framework, DualHyp, empowers a large language model (LLM) to compose independent N-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition (VSR) models. To maximize the effectiveness of DualHyp, we further introduce RelPrompt, a noise-aware guidance mechanism that provides modality-grounded prompts to the LLM. RelPrompt offers the temporal reliability of each modality stream, guiding the model to dynamically switch its focus between ASR and VSR hypotheses for an accurate correction. Under various corruption scenarios, our framework attains up to 57.7% error rate gain on the LRS2 benchmark over standard ASR baseline, contrary to single-stream GER approaches that achieve only 10% gain. To facilitate research within our DualHyp framework, we release the code and the dataset comprising ASR and VSR hypotheses at https://github.com/sungnyun/dualhyp.

Key Contributions

Introduces DualHyp, a novel generative error correction framework for AVSR that leverages an LLM to compose N-best hypotheses from separate ASR and VSR models. It also proposes RelPrompt, a noise-aware guidance mechanism that dynamically directs the LLM's focus based on modality reliability, achieving significant error rate gains over single-stream approaches.

Business Value

Significantly improves the accuracy and robustness of speech recognition systems, leading to better user experiences in voice assistants, transcription services, and communication tools, especially in challenging acoustic environments.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it enhances existing ASR/AVSR pipelines by adding an LLM-based correction module.

Limitations Addressed

Errors in single-modality ASR or VSR,Robustness issues in noisy environments,Ineffectiveness of single-stream error correction methods

Performance Gains

Up to 57.7% error rate gain on LRS2 over standard ASR baseline

Technical Tags

audio-visual speech recognitionerror correctiongenerative error correctionlarge language modelsmulti-modal fusionASRVSRhypothesis generationnoise-aware prompting

Research Topics

Speech RecognitionMultimodal AIError CorrectionLarge Language ModelsSignal Processing

Methods & Architectures

DualHyp frameworkGenerative Error Correction (GER)N-best hypothesis compositionRelPrompt (noise-aware guidance mechanism)Modality-grounded promptsLLM-based reasoning Large Language Models (LLMs)Automatic Speech Recognition (ASR) modelsVisual Speech Recognition (VSR) models

Applications & Tasks

Speech Recognition Human-Computer Interaction Accessibility Improving accuracy of ASR/AVSRRobustness to noise and corruptionEffective fusion of audio and visual speech cues Speech error correctionAudio-visual speech recognitionGenerating corrected transcripts

Datasets & Benchmarks

Datasets

LRS2

Benchmarks

LRS2 benchmark

Error Rate Gain

Related Fields

Speech ProcessingMachine LearningArtificial IntelligenceComputer VisionNatural Language Processing

Keywords

AVSRspeech recognitionerror correctionLLMmultimodalASRVSRhypothesisRelPromptLRS2

Academic Context

#Speech Recognition#Multimodal AI#Error Correction#Large Language Models#Signal Processing

Commercial Potential

Potential Products

More accurate voice assistantsImproved transcription servicesReal-time captioning systemsEnhanced communication tools for hearing impaired

Target Industries

TechnologyMediaTelecommunicationsHealthcare

Use Case Examples

Accurate transcription of meetings in noisy officesReliable voice commands for smart devices in loud environmentsReal-time captioning for live broadcasts with background noise

Competitive Edge

Outperforms single-stream GER approaches by effectively fusing information from both audio and visual modalities through an LLM, leading to substantially higher error correction rates.

Market Opportunity

Large and growing market for speech recognition and transcription technologies.

Revenue Models

Licensing the technology to ASR providersoffering enhanced transcription services.

Resource Requirements

Compute Needs

Moderate to High (requires running ASR, VSR, and LLM)

Data Requirements

Paired audio-visual speech data (e.g., LRS2)

Deployment Constraints

Requires synchronized audio and visual streams; computational cost of running multiple models.

Scalability

Scalability depends on the efficiency of the underlying ASR, VSR, and LLM components.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Licensing

Likely open-source for research purposes.

Patent Potential

High

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