Name
Abstract | ||
|---|---|---|
This paper describes an approach to the use of genetic programming (GP) for the automatic detection of rhythmic stress in spoken New Zealand English. A linear-structured GP system uses speaker independent prosodic features and vowel quality features as terminals to classify each vowel segment as stressed or unstressed. Error rate is used as the fitness function. In addition to the standard four arithmetic operators, this approach also uses several other arithmetic, trigonometric, and conditional functions in the function set. The approach is evaluated on 60 female adult utterances with 703 vowels and a maximum accuracy of 92.61% is achieved. The approach is compared with decision trees (DT) and support vector machines (SVM). The results suggest that, on our data set, GP outperforms DT and SVM for stress detection, and GP has stronger automatic feature selection capability than DT and SVM. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11732242_41 | EvoWorkshops |
Keywords | Field | DocType |
linear-structured gp system,arithmetic operator,stronger automatic feature selection,stress detection,automatic detection,conditional function,fitness function,automatic stress detection,genetic programming,function set,vowel quality feature,rhythmic stress,support vector machine,error rate,decision tree,speech recognition,feature selection | Decision tree,Feature selection,Computer science,Word error rate,Support vector machine,Genetic programming,Speech recognition,Fitness function,Vowel,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
3907 | 0302-9743 | 3-540-33237-5 |
Citations | PageRank | References |
7 | 0.53 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huayang Xie | 1 | 85 | 9.96 |
| Mengjie Zhang | 2 | 3777 | 300.33 |
| Peter Andreae | 3 | 358 | 31.85 |