Name
Abstract | ||
|---|---|---|
Recently, the deep neural networks (DNNs) are successfully adopted into the voice activity detection (VAD) area. However, the performance of the DNN-based VAD is still unsatisfactory in noise environments where the feature subspace of the training database and the test environments are not matched with each other. In this paper, we propose a local feature shift technique which normalizes the feature subspaces over various noise environments. The proposed technique considers the local minimum vectors of the log-Mel filterbank features as noise power estimates and produces feature shift vectors from them. The experimental results in stationary and non-stationary noise environments show that the DNN with the proposed technique outperforms the conventional DNN-based VAD algorithms. |
Year | Venue | Field |
|---|---|---|
2016 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Noise power,Pattern recognition,Subspace topology,Noise measurement,Voice activity detection,Computer science,Filter bank,Speech recognition,Feature extraction,Linear subspace,Artificial intelligence,Deep neural networks |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Gyoon Kang | 1 | 27 | 4.42 |
| Kang Hyun Lee | 2 | 0 | 2.03 |
| Woo Hyun Kang | 3 | 1 | 3.42 |
| Soo Hyun Bae | 4 | 0 | 1.35 |
| Nam Soo Kim | 5 | 638 | 55.85 |