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Context-sensitive machine learning for intelligent human behavior analysis

Intelligent automatic human behavior analysis is an essential precondition for conversational agent systems that aim to enable natural, emotionally sensitive human-computer interaction. This thesis focuses on automatic verbal and non-verbal behavior analysis and introduces novel speech processing and machine learning architectures that are capable of inferring the spoken content as well as the affective state from the speech signal.