Intelligent Conversational Android ERICA Applied to Attentive Listening
and Job Interview
- URL: http://arxiv.org/abs/2105.00403v1
- Date: Sun, 2 May 2021 06:37:23 GMT
- Title: Intelligent Conversational Android ERICA Applied to Attentive Listening
and Job Interview
- Authors: Tatsuya Kawahara, Koji Inoue, Divesh Lala
- Abstract summary: We have developed an intelligent conversational android ERICA.
We set up several social interaction tasks for ERICA, including attentive listening, job interview, and speed dating.
It has been evaluated with 40 senior people, engaged in conversation of 5-7 minutes without a conversation breakdown.
- Score: 41.789773897391605
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Following the success of spoken dialogue systems (SDS) in smartphone
assistants and smart speakers, a number of communicative robots are developed
and commercialized. Compared with the conventional SDSs designed as a
human-machine interface, interaction with robots is expected to be in a closer
manner to talking to a human because of the anthropomorphism and physical
presence. The goal or task of dialogue may not be information retrieval, but
the conversation itself. In order to realize human-level "long and deep"
conversation, we have developed an intelligent conversational android ERICA. We
set up several social interaction tasks for ERICA, including attentive
listening, job interview, and speed dating. To allow for spontaneous,
incremental multiple utterances, a robust turn-taking model is implemented
based on TRP (transition-relevance place) prediction, and a variety of
backchannels are generated based on time frame-wise prediction instead of
IPU-based prediction. We have realized an open-domain attentive listening
system with partial repeats and elaborating questions on focus words as well as
assessment responses. It has been evaluated with 40 senior people, engaged in
conversation of 5-7 minutes without a conversation breakdown. It was also
compared against the WOZ setting. We have also realized a job interview system
with a set of base questions followed by dynamic generation of elaborating
questions. It has also been evaluated with student subjects, showing promising
results.
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