TY - GEN
T1 - FORMOSa speech recognition challenge 2018
T2 - 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018
AU - Liao, Yuan Fu
AU - Hsu, Wu Hua
AU - Lin, Yu Chen
AU - Chang, Yung Hsiang Shawn
AU - Pleva, Matus
AU - Juhar, Jozef
AU - Deng, Guang Feng
N1 - Publisher Copyright:
� 2018 IEEE
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper introduces the Formosa speech recognition (FSR) challenge 2018, presents the provided data profile, evaluation plan and reports the experimental results of the baseline systems. This challenge focuses on spontaneous Taiwanese Mandarin speech recognition (TMSR) and it is based on a real-life, multi-gene broadcast radio speech corpus, NER-Trs-Vol1, selected from the Formosa speech in the wild (FSW) project. To assist participants to establish a good starting system, a set of baseline systems were published based on various deep neural network (DNN) models. NER-Trs-Vol1 is free for participants (noncommercial license), and its corresponding Kaldi recipes for the baselines have been published online. Experimental results show that the combination of NER-Trs-Vol1 and Kaldi recipes is a good resource pack for spontaneous TMSR research and could be used to initialize an advanced semi-supervised training procedure to further improve the recognition performance.
AB - This paper introduces the Formosa speech recognition (FSR) challenge 2018, presents the provided data profile, evaluation plan and reports the experimental results of the baseline systems. This challenge focuses on spontaneous Taiwanese Mandarin speech recognition (TMSR) and it is based on a real-life, multi-gene broadcast radio speech corpus, NER-Trs-Vol1, selected from the Formosa speech in the wild (FSW) project. To assist participants to establish a good starting system, a set of baseline systems were published based on various deep neural network (DNN) models. NER-Trs-Vol1 is free for participants (noncommercial license), and its corresponding Kaldi recipes for the baselines have been published online. Experimental results show that the combination of NER-Trs-Vol1 and Kaldi recipes is a good resource pack for spontaneous TMSR research and could be used to initialize an advanced semi-supervised training procedure to further improve the recognition performance.
KW - Kaldi speech toolkits
KW - Neural networks
KW - Semi-supervised training
KW - Spontaneous speech recognition
UR - http://www.scopus.com/inward/record.url?scp=85065876623&partnerID=8YFLogxK
U2 - 10.1109/ISCSLP.2018.8706700
DO - 10.1109/ISCSLP.2018.8706700
M3 - Conference contribution
AN - SCOPUS:85065876623
T3 - 2018 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 - Proceedings
SP - 270
EP - 274
BT - 2018 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 November 2018 through 29 November 2018
ER -