Name
Abstract | ||
|---|---|---|
Long Short-Term Memory networks (LSTMs) are a component of many state-of-the-art DNN-based speech recognition systems. Dropout is a popular method to improve generalization in DNN training. In this paper we describe extensive experiments in which we investigated the best way to combine dropout with LSTMs specifically, projected LSTMs (LSTMP). We investigated various locations in the LSTM to place the dropout (and various combinations of locations), and a variety of dropout schedules. Our optimized recipe gives consistent improvements in WER across a range of datasets. including Switchboard. TED-LIUM and AMI. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.21437/Interspeech.2017-129 | 18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION |
Keywords | Field | DocType |
speech recognition, LSTM, DNN, dropout, lattice-free MMI | Computer science,Speech recognition | Conference |
ISSN | Citations | PageRank |
2308-457X | 8 | 0.58 |
References | Authors | |
10 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaofeng Cheng | 1 | 8 | 4.97 |
| Vijayaditya Peddinti | 2 | 229 | 12.17 |
| Daniel Povey | 3 | 2442 | 231.75 |
| Vimal Manohar | 4 | 54 | 7.99 |
| Sanjeev Khudanpur | 5 | 2155 | 202.00 |
| Yonghong Yan | 6 | 10 | 6.40 |