Name
Title | ||
|---|---|---|
Exploiting Acoustic and Syntactic Features for Prosody Labeling in a Maximum Entropy Framework |
Abstract | ||
|---|---|---|
In this paper we describe an automatic prosody labeling framework that exploits both language and speech information. We model the syntactic-prosodic informa- tion with a maximum entropy model that achieves an accuracy of 85.2% and 91.5% for pitch accent and boundary tone la- beling on the Boston University Radio News corpus. We model the acoustic- prosodic stream with two dierent mod- els, one a maximum entropy model and the other a traditional HMM. We finally couple the syntactic-prosodic and acoustic- prosodic components to achieve signifi- cantly improved pitch accent and bound- ary tone classification accuracies of 86.0% and 93.1% respectively. Similar experimen- tal results are also reported on Boston Di- rections corpus. |
Year | Venue | Keywords |
|---|---|---|
2007 | HLT-NAACL | maximum entropy model,maximum entropy |
Field | DocType | Citations |
Prosody,Maximum entropy spectral estimation,Maximum-entropy Markov model,Pattern recognition,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Principle of maximum entropy,Syntax | Conference | 10 |
PageRank | References | Authors |
0.82 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vivek Kumar Rangarajan Sridhar | 1 | 144 | 11.21 |
| Srinivas Bangalore | 2 | 1319 | 157.37 |
| Narayanan Shrikanth | 3 | 5558 | 439.23 |