%0 Journal Article
%T VOWEL DURATION MEASUREMENT USING DEEP NEURAL NETWORKS.
%A Adi Y
%A Keshet J
%A Goldrick M
%J IEEE Int Workshop Mach Learn Signal Process
%V 2015
%N 0
%D Sep 2015
%M 29034132
暂无%R 10.1109/MLSP.2015.7324331
%X Vowel durations are most often utilized in studies addressing specific issues in phonetics. Thus far this has been hampered by a reliance on subjective, labor-intensive manual annotation. Our goal is to build an algorithm for automatic accurate measurement of vowel duration, where the input to the algorithm is a speech segment contains one vowel preceded and followed by consonants (CVC). Our algorithm is based on a deep neural network trained at the frame level on manually annotated data from a phonetic study. Specifically, we try two deep-network architectures: convolutional neural network (CNN), and deep belief network (DBN), and compare their accuracy to an HMM-based forced aligner. Results suggest that CNN is better than DBN, and both CNN and HMM-based forced aligner are comparable in their results, but neither of them yielded the same predictions as models fit to manually annotated data.