Preliminary investigation of Boltzmann machine classifiers for speaker recognition

By Themos Stafylakis, Patrick Kenny, Mohammed Senoussaoui, Pierre Dumouchel. 2012

45 citations recorded by the source corpus.

How researchers see this paper

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

1. Li Deng
2. Dong Yu
3. Xiangang Li
4. Jinyu Li
5. Yoshua Bengio
6. Yajie Miao
7. Mirco Ravanelli
8. Xihong Wu

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 ”
• Modeling speaker variability using long short-term memory networks for speech recognition (2015, background): “Recently, some studies have been conducted on using DNNs for speaker recognition, such as the use of convolutional deep belief networks [23] and Boltzmann machine classifiers [24].”
• Speaker recognition by means of Deep Belief Networks (2013, methodology): “Preliminary experiments with DBNs have been reporte d in [10],[11] using i–vectors as input features for DBN based classifiers.”
• Deep neural networks for small footprint text-dependent speaker verification (2014, background): “More recently, some preliminary studies have been conducted on using deep learning for speaker recognition, such as the use of convolutional deep belief networks [15] and Boltzmann machine classifiers [16].”