3 accepted papers
We describe the methodology for the collection and annotation of a large corpus of emotional speech data through crowdsourcing. The corpus offers 187 hours of data from 2,965 subjects. Data includes non-emotional recordings from each subject as well as recordings for five emotions: angry, happy-low-…
Interactive voice technologies can leverage biosignals, such as heart rate (HR), to infer the psychophysiological state of the user. Voice-based detection of HR is attractive because it does not require additional sensors. We predict HR from speech using the SRI BioFrustration Corpus. In contrast to…
Growth in voice-based applications and personalized systems has led to increasing demand for speech- analytics technologies that estimate the state of a speaker from speech. Such systems support a wide range of applications, from more traditional call-center monitoring, to health monitoring, to huma…