Audio-visual speech enhancement using hierarchical extreme learning machine

Recently, the hierarchical extreme learning machine (HELM) model has been utilized for speech enhancement (SE) and demonstrated promising performance, especially when the amount of training data is limited and the system does not support heavy computations. Based on the success of audio-only-based systems, termed AHELM, we propose a novel audio-visual HELM-based SE system, termed AVHELM that integrates the audio and visual information to confrontate the unseen non-stationery noise problem at low SNR levels to attain improved SE performance. The experimental results demonstrate that AVHELM can yield satisfactory enhancement performance with a limited amount of training data and outperforms AHELM in terms of three standardized objective measures under matched and mismatched testing conditions, confirming the effectiveness of incorporating visual information into the HELM-based SE system.