Name
Abstract | ||
|---|---|---|
This paper presents a novel visual speech recognition approach based on motion segmentation and hidden Markov models (HMM). The proposed method identifies utterances from mouth video, without evaluating voice signals. The facial movements in the video data are represented using 2D spatial-temporal templates (STT). The proposed technique combines discrete stationary wavelet transform (SWT) and Zernike moments to extract rotation invariant features from the STTs. HMMs are used as speech classifier to model English phonemes. The preliminary results demonstrate that the proposed technique is suitable for phoneme classification with a high accuracy. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74272-2_103 | CAIP |
Keywords | Field | DocType |
speech classifier,zernike moment,hidden markov model,video data,mouth video,english phoneme,proposed technique,motion feature,novel visual speech recognition,facial movement,discrete stationary wavelet,stationary wavelet transform | Computer vision,Pattern recognition,Segmentation,Computer science,Zernike polynomials,Speech recognition,Invariant (mathematics),Artificial intelligence,Classifier (linguistics),Hidden Markov model,Stationary wavelet transform | Conference |
Volume | ISSN | Citations |
4673 | 0302-9743 | 12 |
PageRank | References | Authors |
0.60 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wai Chee Yau | 1 | 40 | 4.87 |
| Dinesh Kant Kumar | 2 | 168 | 28.34 |
| Hans Weghorn | 3 | 203 | 56.24 |