To create conversational systems with human-like listener behavior, generating short feedback responses (e.g., “mhm”, “ah”, “wow”) appropriate for their context is crucial. These responses convey their communicative function through their lexical form and their prosodic realization. In this paper, we transplant the prosody of feedback responses from human-human U.S. English telephone conversations to a target speaker using two synthesis techniques (TTS and signal processing). Our evaluation focuses on perceived naturalness, contextual appropriateness and preservation of communicative function. Results indicate TTS-generated feedback were perceived as more natural than signal-processing-based feedback, with no significant difference in appropriateness. However, the TTS did not consistently convey the communicative function of the original feedback.

An important objective in health-technology is the ability to gather information about people’s well-being. Structured interviews can be used to obtain this information, but are time-consuming and not scalable. Questionnaires provide an alternative way to extract such information, though typically lack depth. In this paper, we present our first prototype of the BLISS agent, an artificial intelligent agent which intends to automatically discover what makes people happy and healthy. The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people’s happiness by conducting a personalized spoken dialogue based on a happiness model. We built our first prototype of the model to collect 55 spoken dialogues, in which the BLISS agent asked questions to users about their happiness and well-being. Apart from a description of the BLISS architecture, we also provide details about our dataset, which contains over 120 activities and 100 motivations and is made available for usage.