Name
Abstract | ||
|---|---|---|
Identification of prosodic phenomena is of first importance in prosodic analysis and modeling. In this paper, we introduce a new method for automatic prosodic phenomena labelling. The authors set their approach of prosodic phenomena in the framework of prominence. The proposed method for automatic prominence labelling is based on well-known machine learning techniques in a three step procedure: (i) a feature extraction step in which we propose a framework for systematic and multi-level speech acoustic feature extraction, (ii) a feature selection step for identifying the more relevant prominence acoustic correlates, and (iii) a modelling step in which a gaussian mixture model is used for predicting prominence. This model shows robust performance on read speech (84%). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICASSP.2008.4518529 | Las Vegas, NV |
Keywords | Field | DocType |
Gaussian processes,feature extraction,learning (artificial intelligence),natural language processing,speech processing,French prominence,Gaussian mixture model,automatic prosodic phenomena labelling,machine learning,multilevel speech acoustic feature extraction,probabilistic framewok,prosodic analysis,prosodic modeling,prosodic phenomena identification,Prosody,acoustic correlates,classification,feature selection,gaussian mixture model,prominence | Speech processing,Prosody,Feature selection,Pattern recognition,Computer science,Feature extraction,Speech recognition,Gaussian process,Artificial intelligence,Natural language processing,Mixture model,Probabilistic framework | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-1484-0 | 978-1-4244-1484-0 | 5 |
PageRank | References | Authors |
0.78 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Obin | 1 | 68 | 11.76 |
| Xavier Rodet | 2 | 627 | 107.87 |
| Anne Lacheret-Dujour | 3 | 25 | 6.24 |