Real-time voice activity detection for ECoG-based speech brain machine interfaces

Author(s):  
Vasileios G. Kanas ◽  
Iosif Mporas ◽  
Heather L. Benz ◽  
Kyriakos N. Sgarbas ◽  
Anastasios Bezerianos ◽  
...  
ETRI Journal ◽  
2011 ◽  
Vol 33 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Mohammad Hossein Moattar ◽  
Mohammad Mehdi Homayounpour

Author(s):  
Xincheng Gao ◽  
Houbin Cao ◽  
Jianfeng Zhang ◽  
Jinping Bai ◽  
Tianhang Zhang ◽  
...  

Author(s):  
Charaf Eddine Chelloug ◽  
◽  
Atef Farrouki ◽  

In speech compression systems, Voice Activity Detection (VAD) is frequently used to distinguish active voice from other noisy sounds. In this paper, a robust approach of VAD is presented to deal with non-stationary noisy environments. The proposed algorithm exploits adaptive thresholding technique to keep a desired False Acceptance (FA) rate. Iterative hypothesis tests, using signal energy, are implemented to discard or to accept the successive audio frames as active voice. According to the stationary property of the speech, we provide a smoothing method to obtain final VAD decisions. The main contribution of the proposed algorithm concerns its ability to automatically adjust the energy threshold according to the local noise estimator. We analyzed the proposed approach by presenting a comparison with the G.729-B via the NOIZEUS database. The VAD architecture is implemented on a Microcontroller-based system (MCU). Several tests have been conducted by performing real time acquisition via the Input/Output ports of the MCU-system.


Author(s):  
Saurav Dubey ◽  
Arash Mahnan ◽  
Jürgen Konczak

Abstract Speech analysis using microphones can be problematic for Voice Activity Detection (VAD) in the presence of background noise. This study explored the use of wearable accelerometers instead of microphones. We assessed if accelerometers placed on the neck can be part of a VAD system embedded in a wearable collar-like device that delivers vibro-tactile stimulation (VTS) to the larynx during speech as a therapy for patients with the voice disorder spasmodic dysphonia. Specifically, we aimed to a) find the ideal location for placing accelerometers to the neck, and b) develop a VAD algorithm that detects the onset and offset of speech. Six healthy adult participants (M/F = 3/3, age = 26 (5.1)) vocalized 20 sample sentences with and without VTS at three neck locations: 1) thyroid cartilage, 2) sterno-cleidomastoid, and 3) posterior neck above C7. Based on time-synchronized acceleration and audio signals, VAD algorithm identified the Number of Onsets of Speech and Total Time Voiced. The thyroid cartilage attachment location had over 90% accuracy detecting speech in both measures. The average accuracy of the sternocleidomastoid and C7 locations were below 75% and 15% respectively. VAD accuracy decreased with the presence of VTS trials at all locations. We conclude that accelerometer signals due to tissue motion at thyroid cartilage are most suitable for real-time VAD. These findings support the feasibility of accelerometer-based voice detection for the use in medical devices that target speech and voice disorders.


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