| Paper ID | SPE-41.5 |
| Paper Title |
MARBLENET: DEEP 1D TIME-CHANNEL SEPARABLE CONVOLUTIONAL NEURAL NETWORK FOR VOICE ACTIVITY DETECTION |
| Authors |
Fei Jia, Somshubra Majumdar, Boris Ginsburg, NVIDIA Corporation, United States |
| Session | SPE-41: Voice Activity and Disfluency Detection |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-VAD] Voice Activity Detection and End-pointing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We present MarbleNet, an end-to-end neural network for Voice Activity Detection (VAD). MarbleNet is a deep residual network composed from blocks of 1D time-channel separable convolution, batch-normalization, ReLU and dropout layers. When compared to a state-of-the-art VAD model, MarbleNet is able to achieve similar performance with roughly 1/10-th the parameter cost. We further conduct extensive ablation studies on different training methods and choices of parameters in order to study the robustness of MarbleNet in real-world VAD tasks. |
