Abstract
Existing speech classification algorithms often perform well when evaluated on training and test data drawn from the same distribution. In practice, however, these distributions are not always the same. In these circumstances, the performance of trained models will likely decrease. In this paper, we discuss an underutilized divergence measure and derive an estimable upper bound on the test error rate that depends on the error rate on the training data and the distance between training and test distributions. Using this bound as motivation, we develop a feature learning algorithm that aims to identify invariant speech features that generalize well to data similar to, but different from, the training set. Comparative results confirm the efficacy of the algorithm on a set of cross-domain speech classification tasks.
| Original language | English (US) |
|---|---|
| Title of host publication | 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 77-82 |
| Number of pages | 6 |
| ISBN (Print) | 9781479971299 |
| DOIs | |
| State | Published - Apr 1 2014 |
| Event | 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - South Lake Tahoe, United States Duration: Dec 7 2014 → Dec 10 2014 |
Other
| Other | 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 |
|---|---|
| Country | United States |
| City | South Lake Tahoe |
| Period | 12/7/14 → 12/10/14 |
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Keywords
- Domain adaptation
- Feature selection
- Machine learning
- Pathological speech analysis
ASJC Scopus subject areas
- Computer Science Applications
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Language and Linguistics
Cite this
Domain invariant speech features using a new divergence measure. / Wisler, Alan; Berisha, Visar; Liss, Julie; Spanias, Andreas.
2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 77-82 7078553.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Domain invariant speech features using a new divergence measure
AU - Wisler, Alan
AU - Berisha, Visar
AU - Liss, Julie
AU - Spanias, Andreas
PY - 2014/4/1
Y1 - 2014/4/1
N2 - Existing speech classification algorithms often perform well when evaluated on training and test data drawn from the same distribution. In practice, however, these distributions are not always the same. In these circumstances, the performance of trained models will likely decrease. In this paper, we discuss an underutilized divergence measure and derive an estimable upper bound on the test error rate that depends on the error rate on the training data and the distance between training and test distributions. Using this bound as motivation, we develop a feature learning algorithm that aims to identify invariant speech features that generalize well to data similar to, but different from, the training set. Comparative results confirm the efficacy of the algorithm on a set of cross-domain speech classification tasks.
AB - Existing speech classification algorithms often perform well when evaluated on training and test data drawn from the same distribution. In practice, however, these distributions are not always the same. In these circumstances, the performance of trained models will likely decrease. In this paper, we discuss an underutilized divergence measure and derive an estimable upper bound on the test error rate that depends on the error rate on the training data and the distance between training and test distributions. Using this bound as motivation, we develop a feature learning algorithm that aims to identify invariant speech features that generalize well to data similar to, but different from, the training set. Comparative results confirm the efficacy of the algorithm on a set of cross-domain speech classification tasks.
KW - Domain adaptation
KW - Feature selection
KW - Machine learning
KW - Pathological speech analysis
UR - http://www.scopus.com/inward/record.url?scp=84946691492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946691492&partnerID=8YFLogxK
U2 - 10.1109/SLT.2014.7078553
DO - 10.1109/SLT.2014.7078553
M3 - Conference contribution
AN - SCOPUS:84946691492
SN - 9781479971299
SP - 77
EP - 82
BT - 2014 IEEE Workshop on Spoken Language Technology, SLT 2014 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
ER -