Improving real-time voice activity detection for perceptual robotic control in noisy environment

2011 ◽  
Author(s):  
Po-Yi Shih ◽  
Po-Chuan Lin ◽  
Jhing-Fa Wang ◽  
You-Zen Chen
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Robotic Control ◽  
Noisy Environment ◽  
Voice Activity

A Weighted Feature Voting Approach for Robust and Real-Time Voice Activity Detection

ETRI Journal ◽  
2011 ◽  
Vol 33 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Mohammad Hossein Moattar ◽  
Mohammad Mehdi Homayounpour
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Voice Activity

scholarly journals Voice Activity Detection for Speech Enhancement Applications

10.14311/1251 ◽  
2010 ◽  
Vol 50 (4) ◽  
Author(s):  
E. Verteletskaya ◽  
K. Sakhnov
Keyword(s):  
Background Noise ◽  
Voice Activity Detection ◽  
Energy Ratio ◽  
Activity Detection ◽  
Noisy Environment ◽  
Signal Energy ◽  
Low Snr ◽  
Noise Robust ◽  
Voice Activity ◽  
Band Signal

This paper describes a study of noise-robust voice activity detection (VAD) utilizing the periodicity of the signal, full band signal energy and high band to low band signal energy ratio. Conventional VADs are sensitive to a variably noisy environment especially with low SNR, and also result in cutting off unvoiced regions of speech as well as random oscillating of output VAD decisions. To overcome these problems, the proposed algorithm first identifies voiced regions of speech and then differentiates unvoiced regions from silence or background noise using the energy ratio and total signal energy. The performance of the proposed VAD algorithm is tested on real speech signals. Comparisons confirm that the proposed VAD algorithm outperforms the conventional VAD algorithms, especially in the presence of background noise.

A Real-Time DSP-Based System for Voice Activity Detection: Design and Implement

2013 ◽  
Vol 6 (6) ◽  
pp. 27-40 ◽  
Author(s):  
Xincheng Gao ◽  
Houbin Cao ◽  
Jianfeng Zhang ◽  
Jinping Bai ◽  
Tianhang Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Voice Activity

scholarly journals Real Time Implementation of Voice Activity Detection based on False Acceptance Regulation

2020 ◽  
Vol 12 (3) ◽  
pp. 654-666
Author(s):  
Charaf Eddine Chelloug ◽  
◽  
Atef Farrouki ◽  
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Speech Compression ◽  
Noisy Environments ◽  
Signal Energy ◽  
Active Voice ◽  
Voice Activity ◽  
Time Acquisition ◽  
False Acceptance

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.

Real-Time Voice Activity Detection Using Neck-Mounted Accelerometers for Controlling a Wearable Vibration Device to Treat Speech Impairment

2020 ◽  
Author(s):  
Saurav Dubey ◽  
Arash Mahnan ◽  
Jürgen Konczak
Keyword(s):  
Real Time ◽  
Thyroid Cartilage ◽  
Voice Activity Detection ◽  
Voice Disorder ◽  
Activity Detection ◽  
Speech Impairment ◽  
Audio Signals ◽  
Average Accuracy ◽  
Wearable Accelerometers ◽  
Voice Activity

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.

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

2014 ◽  
Author(s):  
Vasileios G. Kanas ◽  
Iosif Mporas ◽  
Heather L. Benz ◽  
Kyriakos N. Sgarbas ◽  
Anastasios Bezerianos ◽  
...  
Keyword(s):  
Real Time ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Brain Machine Interfaces ◽  
Voice Activity
Sign in / Sign up

Export Citation Format

Share Document