Name
Abstract | ||
|---|---|---|
In this work we look at using non-parametric, exemplar-based regression for the prediction of prosodic contour targets from textual features in a speech synthesis system. We investigate the performance of Gaussian Process regression on this task when the covariance kernel operates on a variety of input feature spaces. In particular, we consider non-linear features extracted via Deep Belief Networks. We motivate the use of this hybrid model by considering the initial deep-layer model as a feature extractor that can summarize high-level structure from the raw inputs to improve the regression of an exemplar-based model in the second part of the approach. By looking at both objective metrics and perceptual listening tests, we evaluate these proposals against each other, and against the standard clustering-tree techniques implemented in parametric synthesis for the prediction of prosodic targets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638996 | 2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
speech synthesis, intonation generation, neural networks, Gaussian processes | Kernel (linear algebra),Kriging,Speech synthesis,Pattern recognition,Regression analysis,Computer science,Deep belief network,Feature extraction,Parametric statistics,Artificial intelligence,Gaussian process,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 14 | 0.84 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raul Fernandez | 1 | 34 | 1.59 |
| Asaf Rendel | 2 | 38 | 3.08 |
| Bhuvana Ramabhadran | 3 | 1779 | 153.83 |
| Ron Hoory | 4 | 181 | 19.16 |