This paper evaluates the extent to which current Large Language Models (LLMs) can
capture task-oriented multi-party conversations (MPCs). We have recorded and transcribed …

We have deployed an LLM-based spoken dialogue system in a real hospital. The ARI social
robot embodies our system, which patients and their companions can have multi-party …

Voice assistants interrupt people when they pause mid-question, a frustrating interaction that
requires the full repetition of the entire question again. This impacts all users, but particularly …

Today's conversational robots largely lack multi-party abilities to differ-entiate between
human speakers, identify addressees of an utterance, understand complex social situations …

In spontaneous conversation, speakers seldom have a full plan of what they are going to say
in advance: they need to conceptualise and plan incrementally as they articulate each word …

There have been significant advances in robotics, conversational AI, and spoken dialogue
systems (SDSs) over the past few years, but we still do not find social robots in public spaces …

Voice assistant accessibility is generally overlooked as today's spoken dialogue systems are
trained on huge corpora to help them understand the 'average'user. This raises frustrating …

Speech production is nuanced and unique to every individual, but today's Spoken Dialogue
Systems (SDSs) are trained to use general speech patterns to successfully improve …

Tekoälyä hyödyntävät ääniavusteiset järjestelmät ovat viime vuosien aikana nousseet
merkittävään rooliin teknologiassa. Yhteiskunnan teknologisen kehityksen ja väestön …