Audio-Visual Decision Fusion for WFST-based and seq2seq Models

TL;DR

This paper introduces novel audio-visual fusion methods for speech recognition that improve accuracy under noisy conditions by independently training models and combining their outputs at inference time.

Contribution

It proposes new fusion techniques for WFST-based and seq2seq models that enable independent training and effective inference-time combination of audio and visual data.

Findings

Significant WER reduction over acoustic-only systems at various SNRs

Effective fusion without weighing parameter in seq2seq models

Improved robustness in noisy speech recognition scenarios

Abstract

Under noisy conditions, speech recognition systems suffer from high Word Error Rates (WER). In such cases, information from the visual modality comprising the speaker lip movements can help improve the performance. In this work, we propose novel methods to fuse information from audio and visual modalities at inference time. This enables us to train the acoustic and visual models independently. First, we train separate RNN-HMM based acoustic and visual models. A common WFST generated by taking a special union of the HMM components is used for decoding using a modified Viterbi algorithm. Second, we train separate seq2seq acoustic and visual models. The decoding step is performed simultaneously for both modalities using shallow fusion while maintaining a common hypothesis beam. We also present results for a novel seq2seq fusion without the weighing parameter. We present results at varying SNR and show that our methods give significant improvements over acoustic-only WER.