TY - GEN
T1 - Multi-view features in a DNN-CRF model for improved sentence unit detection on English broadcast news
AU - Huang, Guangpu
AU - Xu, Chenglin
AU - Xiao, Xiong
AU - Xie, Lei
AU - Chng, Eng Siong
AU - Li, Haizhou
N1 - Publisher Copyright:
© 2014 Asia-Pacific Signal and Information Processing Ass.
PY - 2014/2/12
Y1 - 2014/2/12
N2 - This paper presents a deep neural network-conditional random field (DNN-CRF) system with multi-view features for sentence unit detection on English broadcast news. We proposed a set of multi-view features extracted from the acoustic, articulatory, and linguistic domains, and used them together in the DNN-CRF model to predict the sentence boundaries. We tested the accuracy of the multi-view features on the standard NIST RT-04 English broadcast news speech data. Experiments show that the best system outperforms the state-of-the-art sentence unit detection system significantly by 13.2% absolute NIST sentence error rate reduction using the reference transcription. However, the performance gain is limited on the recognized transcription partly due to the high word error rate.
AB - This paper presents a deep neural network-conditional random field (DNN-CRF) system with multi-view features for sentence unit detection on English broadcast news. We proposed a set of multi-view features extracted from the acoustic, articulatory, and linguistic domains, and used them together in the DNN-CRF model to predict the sentence boundaries. We tested the accuracy of the multi-view features on the standard NIST RT-04 English broadcast news speech data. Experiments show that the best system outperforms the state-of-the-art sentence unit detection system significantly by 13.2% absolute NIST sentence error rate reduction using the reference transcription. However, the performance gain is limited on the recognized transcription partly due to the high word error rate.
UR - https://www.scopus.com/pages/publications/84949925559
U2 - 10.1109/APSIPA.2014.7041543
DO - 10.1109/APSIPA.2014.7041543
M3 - 会议稿件
AN - SCOPUS:84949925559
T3 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
BT - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014
Y2 - 9 December 2014 through 12 December 2014
ER -