Deep Belief Networks Based Voice Activity Detection

By Xiao-Lei Zhang, Ji Wu. 2013

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Yoshua Bengio
5. Xiaodong He
6. Zheng-Hua Tan
7. Shinji Watanabe
8. Yifan Gong

In Signal Processing

1. Dong Yu
2. Li Deng
3. Jinyu Li
4. Yifan Gong
5. Zheng-Hua Tan
6. Najim Dehak
7. Yoshua Bengio
8. Chin-Hui Lee

Citations in the corpus

• Deep Learning: Methods and Applications (2014, background): “to emotion recognition from speech in [207, 222], to spoken language understanding in [242, 366, 403], to speaker recognition in [351, 372], to language-type recognition in [112], to dialogue state tracking for spoken dialogue systems in [94, 152], to automatic voice activity detection in [442], to ”
• Forecasting exchange rate using deep belief networks and conjugate gradient method (2015, background): “DBNs and restricted Boltzmann machines (RBMs) have already been applied successfully to solve many problems, such as classification [11,12], traffic flow prediction [13], voice activity detection [14], dimensionality-reduction [15,16], and information retrieval [17].”
• Time series prediction of stock price using deep belief networks with intrinsic plasticity (2017, methodology): “It have already been applied successfully to solve many problems, such as classification[12][13][14], voice activity detection[15][16][17], information retrieval[18], traffic flow prediction[19], and forecasting exchange rate[20].”
• An End-to-End Multimodal Voice Activity Detection Using WaveNet Encoder and Residual Networks (2019, methodology): “Deep neural networks were recently utilized in several modern voice activity detectors; Zhang and Wu [15] proposed to extract a set of predefined acoustic features, e.”
• rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method (2019, methodology): “For the purpose of modelling and classification, popular techniques include Gaussian models [19], Gaussian mixture models (GMM) [2, 14, 20], super-Gaussian models and their convex combination [9], i-vector [15, 21], decision trees [22], support vector machines [23], and neural network models (includ”