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.

BackgroundInternet search is the most common activity on the World Wide Web and generates a vast amount of user-reported data regarding their information-seeking preferences and behavior. Although this data has been successfully used to examine outbreaks, health care utilization, and outcomes related to quality of care, its value in informing public health policy remains unclear.ObjectiveThe aim of this study was to evaluate the role of Internet search query data in health policy development. To do so, we studied the public’s reaction to a major societal event in the context of the 2012 Sandy Hook School shooting incident.MethodsQuery data from the Yahoo! search engine regarding firearm-related searches was analyzed to examine changes in user-selected search terms and subsequent websites visited for a period of 14 days before and after the shooting incident.ResultsA total of 5,653,588 firearm-related search queries were analyzed. In the after period, queries increased for search terms related to “guns” (+50.06%), “shooting incident” (+333.71%), “ammunition” (+155.14%), and “gun-related laws” (+535.47%). The highest increase (+1054.37%) in Web traffic was seen by news websites following “shooting incident” queries whereas searches for “guns” (+61.02%) and “ammunition” (+173.15%) resulted in notable increases in visits to retail websites. Firearm-related queries generally returned to baseline levels after approximately 10 days.ConclusionsSearch engine queries present a viable infodemiology metric on public reactions and subsequent behaviors to major societal events and could be used by policymakers to inform policy development.

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We investigate a representative case of sudden information need change of Web users. By analyzing search engine query logs, we show that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We investigate ways of identifying whether a query is a good candidate for contextualization conditioned on the most recently browsed document by a user. We build a successful classifier for this task, which achieves 96% precision at 90% recall.

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