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Related Experiment Video

Updated: Sep 3, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Reliability-Based Large-Vocabulary Audio-Visual Speech Recognition.

Wentao Yu¹, Steffen Zeiler¹, Dorothea Kolossa¹

¹Institute of Communication Acoustics, Ruhr University Bochum, 44801 Bochum, Germany.

Sensors (Basel, Switzerland)

|July 28, 2022

Summary

This summary is machine-generated.

A new Decision Fusion Net (DFN) improves audio-visual speech recognition (AVSR) by better using visual cues, especially in noisy conditions for large vocabularies.

Keywords:

audio-visual speech recognition decision fusion net end-to-end recognition hybrid models reliability measures

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Area of Science:

Artificial Intelligence
Computer Vision
Speech Processing

Background:

Audio-visual speech recognition (AVSR) enhances performance over audio-only systems for limited vocabularies.
Current AVSR systems, both hybrid and end-to-end (E2E), underutilize visual information, leading to degraded performance in noisy, large-vocabulary scenarios.

Purpose of the Study:

To introduce a novel fusion architecture, the Decision Fusion Net (DFN), to optimize the use of multi-modal information in AVSR.
To improve the robustness and accuracy of large-vocabulary AVSR systems, particularly under adverse acoustic conditions.

Main Methods:

Developed the Decision Fusion Net (DFN) architecture, incorporating time-variant reliability measures as auxiliary inputs.
Integrated the DFN into both hybrid and end-to-end (E2E) AVSR frameworks.
Evaluated the DFN on large-vocabulary datasets: Lip Reading Sentences 2 (LRS2) and 3 (LRS3).

Main Results:

The DFN significantly improved AVSR performance on large-vocabulary datasets compared to existing systems.
The hybrid DFN model surpassed the performance of oracle dynamic stream-weighting.
Achieved relative word error rate reductions of 51% for the hybrid DFN and 43% for the E2E-DFN compared to their respective audio-only baselines.

Conclusions:

The proposed Decision Fusion Net (DFN) architecture effectively enhances audio-visual speech recognition by optimally fusing audio and visual streams.
DFN demonstrates superior performance in large-vocabulary settings and noisy conditions, addressing limitations of current AVSR technologies.
The DFN represents a significant advancement in multi-modal speech recognition research.