Compensation of Lombard effect in speech enhancement

Abstract

Influenced by Lombard effect, the synthetic noisy speech training data commonly used in speech enhancement algorithm based on deep learning is different from the pronunciation under actual noise, which leads to the limited application performance of the algorithm in the actual environment. This paper summarizes the data compensation strategy proposed by researchers for speech enhancement algorithm under Lombard effect. However, these compensation strategies mainly focus on the front-end data processing part of speech enhancement, which improves the complexity of speech enhancement system.