Name
Title | ||
|---|---|---|
Neural Networks and TEO Features for an Automatic Recognition of Stress in Spontaneous Speech |
Abstract | ||
|---|---|---|
This study presents automatic stress recognition methods based on acoustic speech analysis. Novel approaches to feature extraction based on the nonlinear Teager energy operator (TEO) calculated within critical bands, discrete wavelet transform bands, and wavelet packet bands are presented. The classification process was performed using two types of neural networks: the multilayer perceptron neural network (MLPNN) and the probabilistic neural network (PNN). The classification efficiency was tested using the actual stress dataset from the SUSAS database. The speech recordings were made by 15 speakers (8 females and 7 males) reading a list of 35 words under three actual conditions: high stress, low stress, and neutral. The best overall performance was observed for the features extracted using the TEO parameters calculated within perceptual wavelet packet bands (TEO-PWP). Depending on the type of mother wavelet, the correct classification scores for the PWP features ranged from 71.24% to 91.56% (using the MLPNN classifier), and from 86.63% to 93.67% (using the PNN). The PNN classifier outperformed the MLPNN classification method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.56 | ICNC (2) |
Keywords | Field | DocType |
automatic recognition,correct classification score,mother wavelet,automatic stress recognition method,actual stress dataset,mlpnn classification method,classification process,high stress,neural networks,teo features,classification efficiency,discrete wavelet,spontaneous speech,low stress,database management systems,multilayer perceptron,artificial neural networks,probabilistic neural network,speech recognition,feature extraction,speech,discrete wavelet transform,neural network,stress | Computer science,Artificial intelligence,Discrete wavelet transform,Classifier (linguistics),Artificial neural network,Wavelet,Pattern recognition,Critical band,Network packet,Feature extraction,Speech recognition,Probabilistic neural network,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling He | 1 | 52 | 6.94 |
| Margaret Lech | 2 | 239 | 24.84 |
| Namunu C. Maddage | 3 | 345 | 26.51 |
| Nicholas B. Allen | 4 | 38 | 5.19 |