A rapidly growing interest in human-computer interactions and
spoken dialog systems has been observed in the last couple of years.
Chatbots are becoming increasingly predominant, especially for
customer service and personal companions and assistants. Main efforts
are being undertaken on automatic speech recognition (ASR) and natural
language understanding (NLU), facing challenges such as background
noise in rooms, overlapping speech, and understanding context. Besides
recognizing spoken sentences and interpreting users’ intentions, the
dialog strategy should pursue a satisfactory natural and assistive
communication with users. With the need of providing personalized,
tailored solutions based on users' individual behavior and
preferences, adaptive voice-based interactions are today’s focus of
numerous applications in academia and in industry.
In view of the work motivation, the following research questions
can be defined:
1. Identification of user’s positive and negative attributions of
synthetic voices. What speaker social attributions elicit synthetic
voices? How can these attributions be assessed?
2. Definition of acoustic correlates of attributions of synthetic
3. Transformation of voice attributions into negative and positive