TY - GEN
T1 - The SpeDial datasets
T2 - 10th International Conference on Language Resources and Evaluation, LREC 2016
AU - Lopes, José
AU - Chorianopoulou, Arodami
AU - Palogiannidi, Elisavet
AU - Moniz, Helena
AU - Abad, Alberto
AU - Louka, Katerina
AU - Iosif, Elias
AU - Potamianos, Alexandros
PY - 2016
Y1 - 2016
N2 - The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS's development cycle.
AB - The SpeDial consortium is sharing two datasets that were used during the SpeDial project. By sharing them with the community we are providing a resource to reduce the duration of cycle of development of new Spoken Dialogue Systems (SDSs). The datasets include audios and several manual annotations, i.e., miscommunication, anger, satisfaction, repetition, gender and task success. The datasets were created with data from real users and cover two different languages: English and Greek. Detectors for miscommunication, anger and gender were trained for both systems. The detectors were particularly accurate in tasks where humans have high annotator agreement such as miscommunication and gender. As expected due to the subjectivity of the task, the anger detector had a less satisfactory performance. Nevertheless, we proved that the automatic detection of situations that can lead to problems in SDSs is possible and can be a promising direction to reduce the duration of SDS's development cycle.
KW - Emotions
KW - Multi-lingual data
KW - Sentiment analysis
KW - Spoken Dialogue Systems
UR - http://www.scopus.com/inward/record.url?scp=84997195142&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84997195142
T3 - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
SP - 104
EP - 110
BT - Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
A2 - Calzolari, Nicoletta
A2 - Choukri, Khalid
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Grobelnik, Marko
A2 - Odijk, Jan
A2 - Piperidis, Stelios
A2 - Maegaard, Bente
A2 - Mariani, Joseph
PB - European Language Resources Association (ELRA)
Y2 - 23 May 2016 through 28 May 2016
ER -