Multiexpert automatic speech recognition using acoustic and myoelectric signals
Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelectric signals (MES). A new method of combining experts, known as the plausibility method, is employed to combine an acoustic ASR expert and a MES ASR expert. The plausibility method of combining multiple experts, which is based on the mathematical framework of evidence theory, is compared to the Borda count and score-based methods of combination. Acoustic and facial MES data were collected from 5 subjects, using a 10-word vocabulary across an 18-dB range of acoustic noise. As expected the performance of an acoustic expert decreases with increasing acoustic noise; classification accuracies of the acoustic ASR expert are as low as 11.5%. The effect of noise is significantly reduced with the addition of the MES ASR expert. Classification accuracies remain above 78.8% across the 18-dB range of acoustic noise, when the plausibility method is used to combine the opinions of multiple experts. In addition, the plausibility method produced classification accuracies higher than any individual expert at all noise levels, as well as the highest classification accuracies, except at the 9-dB noise level. Using the Borda count and score-based multiexpert systems, classification accuracies are improved relative to the acoustic ASR expert but are as low as 51.5% and 59.5%, respectively.
|Keywords||Classification, Electromyography, Multiexpert system, Myoelectric signals, Speech recognition|
|Journal||IEEE Transactions on Biomedical Engineering|
Chan, A, Englehart, K.B. (Kevin B.), Hudgins, B. (Bernard), & Lovely, D.F. (Dennis F.). (2006). Multiexpert automatic speech recognition using acoustic and myoelectric signals. IEEE Transactions on Biomedical Engineering, 53(4), 676–685. doi:10.1109/TBME.2006.870224