Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
User Modeling and User-Adapted Interaction
Fear-type emotion recognition for future audio-based surveillance systems
Speech Communication
Prometheus database: a multimodal corpus for research on modeling and interpreting human behavior
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
An adaptive framework for acoustic monitoring of potential hazards
EURASIP Journal on Audio, Speech, and Music Processing
Affective speech interface in serious games for supporting therapy of mental disorders
Expert Systems with Applications: An International Journal
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Affect awareness is important for improving human-computer interaction, but also facilitates the detection of a typical behaviours, danger, or crisis situations in surveillance and in human behaviour monitoring applications. The present work aims at the detection and recognition of specific affective states, such as panic, anger, happiness in close to real-world conditions. The affect recognition scheme investigated here relies on an utterance-level audio parameterization technique and a robust pattern recognition scheme based on the Gaussian Mixture Models with Universal Background Modelling (GMM-UBM) paradigm. We evaluate the applicability of the suggested architecture on the PROMETHEUS database, implemented in a number of indoor and outdoor conditions. The experimental results demonstrate the potential of the suggested architecture on the challenging task of affect recognition in real world conditions. However, further enhancement of the affect recognition performance would be needed before any deployment of practical applications.