However, based on an analysis of open text responses, unconcerned reactions seem to be largely explained by knowledge gaps about possible data misuses. After a short educational video on the topic, participants express only moderate privacy concern. Many participants have rarely (28.4%) or never (42.5%) even thought about the possibility of personal information being inferred from speech data. For instance, only 18.7% of participants are at least somewhat aware that physical and mental health information can be inferred from voice recordings.
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Our results show that – while awareness levels vary between different categories of inferred information – there is generally low awareness across all participant demographics, even among participants with professional experience in computer science, data mining, and IT security. We conducted a nationally representative survey in the UK (n = 683, 18-69 years) to investigate people's awareness about the inferential power of voice and speech analysis.
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g., geographical origin, health status, personality). Through voice characteristics and manner of expression, even seemingly benign voice recordings can reveal sensitive attributes about a recorded speaker (e. By fusing participants’ systems, we show that binary classification of alcoholisation and sleepiness from short-term observations, i.e., single utterances, can both reach over 72% accuracy on unseen test data furthermore, we demonstrate that these medium-term states can be recognised more robustly by fusing short-term classifiers along the time axis, reaching up to 91% accuracy for intoxication and 75% for sleepiness.
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This article reviews previous efforts to automatically recognise intoxication and sleepiness from speech signals, and gives an overview on the Challenge conditions and data sets, the methods used by the participants, and their results. Preserving the paradigms of the two previous INTERSPEECH Challenges, researchers were invited to participate in a large-scale evaluation providing unified testing conditions. To bridge this gap on the time axis, and hence broaden the scope of the field, the INTERSPEECH 2011 Speaker State Challenge addressed the algorithmic analysis of medium-term speaker states: alcohol intoxication and sleepiness, both of which are highly relevant in high risk environments.
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In the emerging field of computational paralinguistics, most research efforts are devoted to either short-term speaker states such as emotions, or long-term traits such as personality, gender, or age.