Abstract
In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance degradation under such a situation of insufficient channel information. In order to exploit limited in-domain dataset effectively, we propose an unsupervised domain adaptation approach using Autoencoder based Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a denoising autoencoder to adapt resource-rich development dataset to test domain. The proposed technique is evaluated on the Domain Adaptation Challenge 13 experimental protocols that is widely used in speaker recognition for domain mismatched condition. The results show significant improvements over baselines and results from other prior studies.
| Original language | English |
|---|---|
| Pages (from-to) | 1014-1018 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2017-August |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
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Keywords
- Autoencoder
- Denoising autoencoder
- Domain mismatch
- Speaker recognition
- Unsupervised domain adaptation
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation
Cite this
Autoencoder based domain adaptation for speaker recognition under insufficient channel information. / Shon, Suwon; Mun, Seongkyu; Kim, Wooil; Ko, Hanseok.
In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Vol. 2017-August, 01.01.2017, p. 1014-1018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Autoencoder based domain adaptation for speaker recognition under insufficient channel information
AU - Shon, Suwon
AU - Mun, Seongkyu
AU - Kim, Wooil
AU - Ko, Hanseok
PY - 2017/1/1
Y1 - 2017/1/1
N2 - In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance degradation under such a situation of insufficient channel information. In order to exploit limited in-domain dataset effectively, we propose an unsupervised domain adaptation approach using Autoencoder based Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a denoising autoencoder to adapt resource-rich development dataset to test domain. The proposed technique is evaluated on the Domain Adaptation Challenge 13 experimental protocols that is widely used in speaker recognition for domain mismatched condition. The results show significant improvements over baselines and results from other prior studies.
AB - In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance degradation under such a situation of insufficient channel information. In order to exploit limited in-domain dataset effectively, we propose an unsupervised domain adaptation approach using Autoencoder based Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a denoising autoencoder to adapt resource-rich development dataset to test domain. The proposed technique is evaluated on the Domain Adaptation Challenge 13 experimental protocols that is widely used in speaker recognition for domain mismatched condition. The results show significant improvements over baselines and results from other prior studies.
KW - Autoencoder
KW - Denoising autoencoder
KW - Domain mismatch
KW - Speaker recognition
KW - Unsupervised domain adaptation
UR - http://www.scopus.com/inward/record.url?scp=85039154266&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039154266&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2017-49
DO - 10.21437/Interspeech.2017-49
M3 - Article
VL - 2017-August
SP - 1014
EP - 1018
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SN - 2308-457X
ER -