Enhanced Factored Three-Way Restricted Boltzmann Machines for Speech Detection

Understand

In this letter, we propose enhanced factored three way restricted Boltzmann machines (EFTW-RBMs) for speech detection.

• The proposed model incorporates conditional feature learning by multiplying the dynamical state of the third unit, which allows a modulation over the visible-hidden node pairs.
• Instead of stacking previous frames of speech as the third unit in a recursive manner, the correlation related weighting coefficients are assigned to the contextual neighboring frames.
• Specifically, a threshold function is designed to capture the long-term features and blend the globally stored speech structure.