A new training algorithm called the Approximated Maximum Mutual Information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

Additional Metadata
Keywords Approximated maximum mutual information, Hidden Markov models, Maximum likelihood, Myoelectric signal, Speech recognition
Persistent URL dx.doi.org/10.1109/IEMBS.2006.259992
Conference 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Citation
Guo, H.J. (Hua J.), & Chan, A. (2006). Approximated mutual information training for speech recognition using myoelectric signals. Presented at the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06. doi:10.1109/IEMBS.2006.259992