The Influence of Pleasant and Unpleasant Odours on the Acoustics of Speech

Olfaction, i. e., the sense of smell is referred to as the ‘emotional sense’, as it has been shown to elicit affective responses. Yet, its influence on speech production has not been investigated. In this paper, we introduce a novel speech-based smell recognition approach, drawing from the fields of speech emotion recognition and personalised machine learning. In particular, we collected a corpus of 40 female speakers reading 2 short stories while either no scent, unpleasant odour (fish), or pleasant odour (peach) is applied through a nose clip. Further, we present a machine learning pipeline for the extraction of data representations, model training, and personalisation of the trained models. In a leave-one-speaker-out cross-validation, our best models trained on state-of-the-art wav2vec features achieve a classification rate of 68 % when distinguishing between speech produced under the influence of negative scent and no applied scent. In addition, we highlight the importance of personalisation approaches, showing that a speaker-based feature normalisation substantially improves performance across the evaluated experiments. In summary, the presented results indicate that odours have a weak, but measurable effect on the acoustics of speech.