Improved phoneme-based myoelectric speech recognition.
This paper introduces an enhanced phoneme-based myoelectric signal (MES) speech recognition system. The system can recognize new words without retraining the phoneme classifier, which is considered to be the main advantage of phoneme-based speech recognition. It is shown that previous systems experience severe performance degradation when new words are added to a testing dataset. To maintain high accuracy with new words, several improvements are proposed. In the proposed MES speech recognition approach, the raw MES is processed by class-specific rotation matrices to spatially decorrelate the data prior to feature extraction in a preprocessing stage. Then, an uncorrelated linear discriminant analysis is used for dimensionality reduction. The resulting data are classified through a hidden Markov model classifier to obtain the phonemic log likelihoods of the phonemes, which are mapped to corresponding words using a word classifier. An average word classification accuracy of 98.533% is achieved over six subjects. The system offers dramatically improved accuracy when expanding a vocabulary, offering promise for robust large-vocabulary myoelectric speech recognition.
Zhou
Quan
Q
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada. t8v11@unb.ca
Jiang
Ning
N
Englehart
Kevin
K
Hudgins
Bernard
B
D004576:Electromyography / Q000379:methods*; D005152:Facial Muscles / Q000502:physiology; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D013060:Speech / Q000502:physiology*; D013068:Speech Production Measurement / Q000379:methods*; D049250:Speech Recognition Software