Traditional voice activity detectors (VADs) tend to be deaf to the acoustical background noise, as they (i) utilize a single operating point for all SNRs (signal-to-noise ratios) and noise types, and (ii) attempt to learn the background noise model online from finite data length. In this paper, we address the aforementioned issues by designing an environmentally aware (EA) VAD. The EA VAD schem...

Voice Activity Detectors (VAD) are important components in audio processing algorithms. In general, VADs are two way classifiers, flagging the audio frames where we have voice activity. Most of them are based on the signal energy and build statistical models of the noise background and the speech signal. In the process of derivation, we are limited to simplified statistical models and this limi...

We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contr...

A novel Voice Activity Detector is presented that is based on Source Separation techniques applied to single sensor signals. It ooers very accurate estimation of the endpoints in very low Signal to Noise ratio conditions, while maintaining low complexity. Since the procedure is totally iterative, it is suitable for use in real-time applications and is capable of operating in dynamically adaptin...

The paper presents a novel approach for voice activity detection. The main idea behind the presented approach is to use, next to the likelihood ratio of a statistical model-based voice activity detector, a set of informative distinct features in order to, via a supervised learning approach, enhance the detection performance. The statistical model-based voice activity detector, which is chosen b...

In this paper we develop a voice activity detection algorithm based on the likelihood that only noise is present in the current signal frame. For this we exploit the fact that the Fourier coefficients of most noise processes can be modeled as statistically independent Gaussian random variables. We also give an overview of different voice activity detectors previously described in the literature...

A combined three-microphone voice activity detector (VAD) and noise-canceling system is studied to enhance speech recognition in an automobile environment. A previous experiment clearly shows the ability of the composite system to cancel a single noise source outside of a defined zone. This paper investigates the performance of the composite system when there are frequently moving noise sources...

This paper presents a model-based voice activity detector (VAD) aimed at operating in low signal to noise ratio conditions and non-stationary noise environments. The proposed system makes use of Gaussian mixture models trained on Mel Frequency Cepstral Coefficients extracted from noisy speech data. In addition, information from smoothed frame based log energy is used to augment the system to de...

This paper proposes a statistical voice activity detector (VAD) suitable for videoconferencing applications, where detection of higher level speech activities, e.g., sentences instead of syllables, words, phrases, etc, is useful. The proposed method uses two distinct features for VAD, energy and entropy in the decorrelated domain, which are modeled as chi-square and Gaussian distributions respe...