Wavelet-based Front-End for Electromyographic Speech Recognition
| Autor: | Tanja Schultz, Michael Wand, Szu-Chen Stan Jou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Discrete wavelet transform
Lifting scheme business.industry Computer science Speech recognition Stationary wavelet transform Second-generation wavelet transform DATA processing & computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Wavelet transform Pattern recognition Wavelet packet decomposition Wavelet Artificial intelligence ddc:004 Fast wavelet transform business Continuous wavelet transform |
| Zdroj: | Scopus-Elsevier INTERSPEECH |
| DOI: | 10.5445/ir/1000008829 |
| Popis: | In this paper we present our investigations on the potential of wavelet-based preprocessing for surface electromyographic speech recognition. We implemented several variants of the Discrete Wavelet Transform and applied them to electromyographical data. First we examined different transforms with various filters and decomposition levels and found that the Redundant Discrete Wavelet Transform performs the best among all tested wavelet transforms. Furthermore, we compared the best wavelet transform to our EMG optimized spectral- and timedomain features. The results showed that the best wavelet transform slightly outperforms the optimized features with 30.9% word error rate compared to 32% for the optimized EMG spectral and time-domain features. Both numbers were achieved on a 108 word vocabulary test set using phone based acoustic models trained on continuously spoken speech captured by EMG. Index Terms: Electromyography, Wavelets, Speech Recognition, Preprocessing |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|