Abstract
Conventional voice-driven wheelchairs usually employ headset microphones that are capable of achieving sufficient recognition accuracy, even in the presence of surrounding noise. However, such interfaces require users to wear sensors such as a headset microphone, which can be an impediment, especially for the hand disabled. Conversely, it is also well known that the speech recognition accuracy drastically degrades when the microphone is placed far from the user. In this paper, we develop a noise robust speech recognition system for a voice-driven wheelchair. This system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors. We verified the effectiveness of our system in experiments in different environments, and confirmed that our system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors.
Highlights
Various voice-driven wheelchairs have already been developed to enable disabled people to move independently, conventional voice-driven wheelchairs still have some associated problems [1,2,3,4]
The Signal-to-Noise Ratio (SNR) of the noise-corrupted speech signals of the headset microphone are evaluated by the following equations: we compare the results of Method B and Method C, we can say that feature compensation is more effective on these environmental noises than is the microphone array
These results imply that the accuracy of Voice Activity Detection (VAD) based on the microphone array is almost the same as that in manual segmentation
Summary
Various voice-driven wheelchairs have already been developed to enable disabled people to move independently, conventional voice-driven wheelchairs still have some associated problems [1,2,3,4]. Conventional voice-driven wheelchairs employ a headset microphone that can record the user’s voice command in a higher Signal-to-Noise Ratio (SNR), even in the presence of surrounding noise, and can achieve sufficient speech recognition accuracy. When the headset microphone moves away from the position of the mouth, users need to be able to adjust the position of the headset microphone by themselves These actions are not always easy, especially for the hand disabled, who are one of the major users of this wheelchair. We develop a noise robust speech recognition system for a voice-driven wheelchair [5] This system can achieve almost the same recognition accuracy as the headset microphone without wearing sensors. Our system can be applied to a variety of noise environments
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