Continuous electromyographic speech recognition with a multi-stream decoding architecture
| Autor: | Szu-Chen Stan Jou, Tanja Schultz, Alex Waibel |
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| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Vocabulary
business.industry Computer science media_common.quotation_subject Speech recognition Speech coding Feature extraction DATA processing & computer science Word error rate Pattern recognition Noise Feature (computer vision) Loudspeaker Artificial intelligence ddc:004 business Decoding methods media_common |
| Zdroj: | ICASSP (4) |
| Popis: | In our previous work, we reported a surface electromyographic (EMG) continuous speech recognition system with a novel EMG feature extraction method, E4, which is more robust to EMG noise than traditional spectral features. In this paper, we show that articulatory feature (AF) classifiers can also benefit from the E4 feature, which improve the F-score of the AF classifiers from 0.492 to 0.686. We also show that the E4 feature is less correlated across EMG channels and thus channel combination gains larger improvement in F-score. With a stream architecture, the AF classifiers are then integrated into the decoding framework and improve the word error rate by 11.8% relative from 33.9% to 29.9%. |
| Databáze: | OpenAIRE |
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