Automatic Smoker Detection from Telephone Speech Signals

This paper proposes an automatic smoking habit detection from spontaneous telephone speech signals. In this method, each utterance is modeled using i-vector and non-negative factor analysis (NFA) frameworks, which yield low-dimensional representation of utterances by applying factor analysis on Gaussian mixture model means and weights respectively. Each framework is evaluated using different classification algorithms to detect the smoker speakers. Finally, score-level fusion of the i-vector-based and the NFA-based recognizers is considered to improve the classification accuracy. The proposed method is evaluated on telephone speech signals of speakers whose smoking habits are known drawn from the National Institute of Standards and Technology (NIST) 2008 and 2010 Speaker Recognition Evaluation databases. Experimental results over 1194 utterances show the effectiveness of the proposed approach for the automatic smoking habit detection task.