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Robust Muscle Activity Onset Detection Using an ...
The GMM consists of two Gaussian distributions, modeling either noise or surface EMG signals. Such machine learning based techniques have recently demonstrated their superiority in discriminating speech signal from background noise for voice activity detection [19–21]. In this study, the unsupervised learning based on a sequential GMM was ......
Real‐time implementation and performance evaluation of ...
methods considered. A noise robust voice activity detection system based on an unsupervised method was proposed by Ali and Talha [18], in which the long-term features were computed using the Katz algorithm of fractal dimension. The signal-to-noise ratio (SNR) was calculated at different levels...
 Voice Activity Detection with Generalized Gamma Distribution
work, we are interested in offline multi-pass voice activity detection (VAD) algorithms suitable for speech communication applications applications such as automatic transcription and speech segmentation. Our goal is to implement and evaluate a robust algorithm for noisy environments, various classes of noise, and short frames....
Performance Evaluation of Silence-Feature Normalization ...
Choi, G. K., & Kim, S. H. (2009). Voice activity detection method using psycho-acoustic model based on speech energy maximization in noisy environments. The Journal of the Acoustical Society of Korea, 28(5), 447–453. Google Scholar
Voice Activity Detection in Noisy Environments Based on ...
A new voice activity detector for noisy environments is proposed. In conventional algorithms, the endpoint of speech is found by applying an edge detection filter that finds the abrupt changing point in a feature domain. However, since the frame energy feature is unstable in noisy environments, it is difficult to accurately find the endpoint of speech....
Features for voice activity detection: a comparative ...
Voice activity detection usually addresses a binary decision on the presence of speech for each frame of the noisy signal. Approaches that locate speech portions in time and frequency domain, such as speech presence probability (SPP) or ideal binary mask (IBM) estimation, can be considered as extensions of VAD that exceed the scope of this article....
VOICE ACTIVITY DETECTION WITH GENERALIZED GAMMA DISTRIBUTION
modelling for voice activity detection. Using a computationally inexpensive maximum likelihood approach, we employ the Bayesian Information Criterion for identifying the phoneme boundaries in noisy speech. 1. INTRODUCTION A common problem in many areas of speech processing is the identification of the presence or absence of a voice...
A Hierarchical Framework Approach for Voice Activity ...
The well-known noise reduction algorithms, such as Wiener filter (WF) algorithms, are widely used for robust voice activity detection which is the critical step for speech recognition. Currently, most of the WF-based algorithms are iterative since the estimation of power spectrum of the clean speech is required in the formulation.
Boosting Contextual Information for Deep Neural Network ...
Network Based Voice Activity Detection Xiao-Lei Zhang, Member, IEEE and DeLiang Wang, Fellow, IEEE Abstract—Voice activity detection (VAD) is an important topic in audio signal processing. Contextual information is important for improving the performance of VAD at low signal-to-noise ratios. Here we explore contextual information by machine...
Speech Activity Detection on YouTube Using Deep Neural ...
Speech activity detection (SAD) is an important first step in speech processing. Commonly used methods (e.g., frame-level classification using gaussian mixture models (GMMs)) work well under stationary noise conditions, but do not generalize well to domains such as YouTube, where videos may exhibit a diverse range of environmental conditions....
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