TY - GEN
T1 - Text-independent phoneme segmentation via learning critical acoustic change points
AU - Teng, Peng
AU - Liu, Xiabi
AU - Jia, Yunde
PY - 2013
Y1 - 2013
N2 - The conventional methods of automatic text-independent phoneme segmentation detect phoneme boundaries via calculating the acoustic changes along speech signals followed by a peak picking procedure according to user-defined rules. Instead, this paper presents a learning-based method in which the phoneme boundaries are viewed as critical points in the acoustic change context of speech signals. First, we adopt a metric learning procedure in the calculation of acoustic changes, in order to make the acoustic changes at phoneme boundaries more discriminative. Then, latent-dynamic conditional random field is used to model the acoustic change context of speech signals for the detection of phoneme boundaries. The experiments demonstrate that our method outperforms the rule-based methods reported in previous work.
AB - The conventional methods of automatic text-independent phoneme segmentation detect phoneme boundaries via calculating the acoustic changes along speech signals followed by a peak picking procedure according to user-defined rules. Instead, this paper presents a learning-based method in which the phoneme boundaries are viewed as critical points in the acoustic change context of speech signals. First, we adopt a metric learning procedure in the calculation of acoustic changes, in order to make the acoustic changes at phoneme boundaries more discriminative. Then, latent-dynamic conditional random field is used to model the acoustic change context of speech signals for the detection of phoneme boundaries. The experiments demonstrate that our method outperforms the rule-based methods reported in previous work.
KW - Latent-Dynamic Conditional Random Field
KW - Metric Learning
KW - Text-Independent Phoneme Segmentation
UR - http://www.scopus.com/inward/record.url?scp=84892880617&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-42057-3_8
DO - 10.1007/978-3-642-42057-3_8
M3 - Conference contribution
AN - SCOPUS:84892880617
SN - 9783642420566
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 61
BT - Intelligence Science and Big Data Engineering - 4th International Conference, IScIDE 2013, Revised Selected Papers
PB - Springer Verlag
T2 - 4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013
Y2 - 31 July 2013 through 2 August 2013
ER -