Lombard Effect

The Lombard effect describes how speakers unconsciously adjust their vocal effort in response to background noise to improve speech intelligibility. Current research investigates this effect across various languages and contexts, exploring how different noise types and levels influence vocal adjustments and examining the role of spectro-temporal features in speech recognition models. This research is significant for advancing our understanding of human speech adaptation and has implications for improving speech recognition technology, particularly in noisy environments, and for developing more robust and adaptable AI models. Furthermore, studies are exploring analogous effects in other domains, such as the impact of data imbalances on machine learning algorithms.