The number of people with physical disabilities is increasing year by year, and the trend of population aging is more and more serious. In order to improve the quality of the life, a control system of accessible home environment for the patients with serious disabilities was developed to control the home electrical devices with the voice of the patients. The control system includes a central control platform, a speech recognition module, a terminal operation module, etc. The system combines the speech recognition control technology and wireless information transmission technology with the embedded mobile computing technology, and interconnects the lamp, electronic locks, alarms, TV and other electrical devices in the home environment as a whole system through a wireless network node. The experimental results showed that speech recognition success rate was more than 84% in the home environment.
Computers ; Disabled Persons ; Humans ; Speech Recognition Software ; Wireless Technology

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.
Artificial Intelligence ; Humans ; Pattern Recognition, Automated ; methods ; Signal Processing, Computer-Assisted ; Speech ; Speech Production Measurement ; methods ; Speech Recognition Software

A new software therapy instrument is proposed for the rehabilitation of aphasia caused by cerebral disorder, which is different from general drug therapy or physical therapy and is, based on modern speech and biofeedback principles. Aphasia rehabilitation software package on the therapy instrument were designed and implement. The features of the software and its future application were discussed.
Aphasia ; rehabilitation ; Communication Aids for Disabled ; Humans ; Software Design ; Speech Recognition Software ; Speech Therapy ; instrumentation ; methods ; Therapy, Computer-Assisted ; methods