Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jeesoo Bang"'
Publikováno v:
Journal of KIISE. 49:60-66
Publikováno v:
Pattern Recognition Letters. 140:230-237
Although listening to a conversation partner is a key factor in the success of dialogue systems or conversational agents, recent neural conversation systems have no interest in generating listening-oriented responses. In this paper, we propose an end
Publikováno v:
ACM Transactions on Asian Language Information Processing. 13:1-21
This article presents an approach to nonnative pronunciation variants modeling and prediction. The pronunciation variants prediction method was developed by generalized transformation-based error-driven learning (GTBL). The modified goodness of pronu
Publikováno v:
ASRU
We built a personalized example-based dialog system that constructs its responses by considering entities that the user has uttered, and topics in which the user has expressed interest. The system analyzes user input utterances, then uses DBpedia and
Publikováno v:
BigComp
This study introduces an example-based chat-oriented dialogue system with personalization framework using long-term memory. Previous representative chat-bots use simple keyword and pattern matching methodologies. To maintain the quality of systems, g
Publikováno v:
Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction ISBN: 9783319155562
MA3HMI@INTERSPEECH
MA3HMI@INTERSPEECH
This study introduces a personalization framework for dialog systems. Our system automatically collects user-related facts (i.e. triples) from user input sentences and stores the facts in one-shot memory. The system also keeps track of changes in use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::270898db5e18284cfb97f6d17fdb1192
https://doi.org/10.1007/978-3-319-15557-9_8
https://doi.org/10.1007/978-3-319-15557-9_8
Autor:
Seonyeong Park, Gary Geunbae Lee, Sangjun Koo, Jeesoo Bang, Junhwi Choi, Kyusong Lee, Seonghan Ryu, Yong Hee Kim
Publikováno v:
Multimodal Analyses enabling Artificial Agents in Human-Machine Interaction ISBN: 9783319155562
MA3HMI@INTERSPEECH
MA3HMI@INTERSPEECH
We proposed an automatic speech recognition (ASR) error correction method using hybrid word sequence matching and recurrent neural network for dialog system applications. Basically, the ASR errors are corrected by the word sequence matching whereas t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5758080da308ea7d91aca3448b155325
https://doi.org/10.1007/978-3-319-15557-9_7
https://doi.org/10.1007/978-3-319-15557-9_7
Publikováno v:
SIGDIAL Conference
We developed a natural language dialog listening agent that uses a knowledge base (KB) to generate rich and relevant responses. Our system extracts an important named entity from a user utterance, then scans the KB to extract contents related to this