Determining the best Acoustic Features for Smoker Identification

Abstract

Speech-based automatic smoker identification (also known as smoker/non-smoker classification) aims to identify speakers’ smoking status from their speech. In the COVID-19 pandemic, speech-based automatic smoker identification approaches have received more attention in smoking cessation research due to low cost and contactless sample collection. This study focuses on determining the best acoustic features for smoker identification. In this paper, we investigate the performance of four acoustic feature sets/representations extracted using three feature extraction/learning approaches: (i) hand-crafted feature sets including the extended Geneva Minimalistic Acoustic Parameter Set and the Computational Paralinguistics Challenge Set, (ii) the Bag-of-Audio-Words representations, (iii) the neural representations extracted from raw waveform signals by SincNet. Experimental results show that: (i) SincNet feature representations are the most effective for smoker identification and outperform the MFCC baseline features by 16% in absolute accuracy; (ii) the performance of hand-crafted feature sets and the Bag-of-Audio-Words representations rely on the scale of the dimensions of feature vectors.