Name
Abstract | ||
|---|---|---|
This paper proposes a hierarchical framework, which consists of three layers of classifiers, for automatic stress detection in English speech utterances. The top two layers are a linguistic classifier, which assigns stressed labels to all content words and unstressed labels to all functions words, and an acoustic classifier, which assigns stressed and unstressed labels with HMM based models and using only acoustic features such as MFCC, energy and f0. When there is no manual stressed label available, only the top two layers are activated. The best performance we achieved is 92.9%. The third layer in the framework is an AdaBoost classifier that can improve the accuracy by using more features and manual labels. The best result we obtained is 94.1%, which is approaching to the self-agreement ratio (97.4%) of the same annotator, or the upper bound of the performance. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660130 | ICASSP (1) |
Keywords | Field | DocType |
stress,training data,upper bound,labeling,mel frequency cepstral coefficient,information science,hidden markov models,speech synthesis | Training set,Mel-frequency cepstrum,Speech synthesis,Pattern recognition,Computer science,Upper and lower bounds,Speech recognition,Artificial intelligence,Hidden Markov model,Classifier (linguistics),Adaboost classifier | Conference |
Volume | ISSN | ISBN |
1 | 1520-6149 | 1-4244-0469-X |
Citations | PageRank | References |
5 | 0.52 | 7 |
Authors | ||
5 | ||