TY - JOUR
T1 - Prosody boundary detection through context-dependent position models
AU - Hu, Yue Ning
AU - Chu, Min
AU - Huang, Chao
AU - Zhang, Yan Ning
PY - 2008
Y1 - 2008
N2 - In this paper, we propose to convert the prosody boundary detection task into a syllable position labeling task. In order to detect both prosodic word and prosodic phrase boundaries, 6 types of syllable positions are defined. For each position, context-dependent position models are trained from manually labeled data. These models are used to label syllable positions in unseen speech. Word and phrase boundaries are then easily derived from syllable position labels. The proposed approach is tested with a large scale single speaker database. The precision and recall for word boundary are 96.1% and 90.1%, respectively, and for phrase boundary are 83.7% and 80.5%, respectively. Results of a listening test shows that only 28% of word boundaries and 50% of phrase of boundaries detected automatically are critical error, implying only about 2.2% and 10% errors for word and phrase boundaries, respectively. The results are rather good, especially when it is considered that only acoustic features are used in this work.
AB - In this paper, we propose to convert the prosody boundary detection task into a syllable position labeling task. In order to detect both prosodic word and prosodic phrase boundaries, 6 types of syllable positions are defined. For each position, context-dependent position models are trained from manually labeled data. These models are used to label syllable positions in unseen speech. Word and phrase boundaries are then easily derived from syllable position labels. The proposed approach is tested with a large scale single speaker database. The precision and recall for word boundary are 96.1% and 90.1%, respectively, and for phrase boundary are 83.7% and 80.5%, respectively. Results of a listening test shows that only 28% of word boundaries and 50% of phrase of boundaries detected automatically are critical error, implying only about 2.2% and 10% errors for word and phrase boundaries, respectively. The results are rather good, especially when it is considered that only acoustic features are used in this work.
KW - Boundary detection
KW - Context-dependent position model
KW - Phrase
KW - Prosodic word
UR - http://www.scopus.com/inward/record.url?scp=84867209316&partnerID=8YFLogxK
M3 - 会议文章
AN - SCOPUS:84867209316
SN - 2308-457X
SP - 2142
EP - 2145
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
T2 - INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association
Y2 - 22 September 2008 through 26 September 2008
ER -