To provide an efficient means of communication for those who cannot move muscles of the whole body except eyes due to amyotrophic lateral sclerosis (ALS), we are developing a speech synthesis interface that is based on electrooculogram (EOG) input. EOG is an electrical signal that is observed through electrodes attached on the skin around eyes and reflects eye position. A key component of the system is a continuous recognizer for the EOG signal. In this paper, we propose and investigate a hidden Markov model (HMM) based EOG recognizer applying continuous speech recognition techniques. In the experiments, we evaluate the recognition system both in user dependent and independent conditions. It is shown that 96.1% of recognition accuracy is obtained for five classes of eye actions by a user dependent system using six channels. While it is difficult to obtain good performance by a user independent system, it is shown that maximum likelihood linear regression (MLLR) adaptation helps for EOG recognition.

Index Terms: electrooculogram, hidden Markov model, amyotrophic lateral sclerosis, continuous speech recognition, maximum likelihood linear regression