The quality of concatenated speech depends on the degree of mismatch between successive units. Defining a perceptually salient join cost to represent the degree of mismatch has proven to be a difficult task. Such a join cost is critical in unit selection synthesis to ensure that the optimum sequence of speech units is selected from the units available in the speech inventory. In this study the problem of defining a join cost is extended to include a feature transformation stage. Two feature transformations are considered, principal component analysis and a neural networkbased approach. Each transformation was investigated for its ability to improve the detection of discontinuities in concatenated speech for a given feature set. The results indicate that a feature transformation combining principal component analysis as a preprocessing stage to a neural network-based transformation can increase the rate of detection of discontinuities. The neural network was trained using perceptual data obtained from a subjective listening test indicating if a join is continuous or discontinuous. The highest scoring measure based on this strategy provided a correlation with perceptual results of 0.8859 compared with a value of 0.7576 over the baseline MFCC measure on the same test data set.
Cite as: Kirkpatrick, B., O'Brien, D., Scaife, R. (2007) Feature transformation applied to the detection of discontinuities in concatenated speech. Proc. 6th ISCA Workshop on Speech Synthesis (SSW 6), 17-21
@inproceedings{kirkpatrick07_ssw,
author={Barry Kirkpatrick and Darragh O'Brien and Ronán Scaife},
title={{Feature transformation applied to the detection of discontinuities in concatenated speech}},
year=2007,
booktitle={Proc. 6th ISCA Workshop on Speech Synthesis (SSW 6)},
pages={17--21}
}