The problem of detecting psychological stress from speech is challenging due to differences in how speakers convey stress. Changes in speech production due to speaker state are not linearly dependent on changes in stress. Research is further complicated by the existence of different stress types and the lack of metrics capable of discriminating stress levels. This study addresses the problem of automatic detection of speech under stress using a previously developed feature extraction scheme based on the Teager Energy Operator (TEO). To improve detection performance a (i) selected sub-band frequency partitioned weighting scheme and (ii) weighting scheme for all frequency bands are proposed. Using the traditional TEO-based feature vector with a closed-speaker Hidden Markov Model-trained stressed speech classifier, error rates of 22.5/13.0% for stress/neutral speech are obtained. With the new weighted sub-band detection scheme, closed-speaker error rates are reduced to 4.7/4.6% for stress/neutral detection, with a relative error reduction of 79.1/64.6%, respectively. For the open-speaker case, stress/neutral speech detection error rates of 69.7/16.2% using traditional features are used to 13.1/4.0% (a relative 81.3/75.4% reduction) with the proposed automatic frequency sub-band weighting scheme. Finally, issues related to speaker dependent/independent scenarios, vowel duration, and mismatched vowel type on stress detection performance are discussed.
To access the full article, please see PDF.