Vowel Duration Measurement Using Deep Neural Networks
Yossi Adi, Joseph Keshet, Matthew Goldrick

Abstract:
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 net- work (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.