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Attentive Adversarial Learning for Domain-Invariant Training

Abstract: 

Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network (DNN) acoustic model and is trained to improve their domain-invariance by optimizing an adversarial loss function. In this work, we propose an attentive ADIT (AADIT) in which we advance the domain classifier with an attention mechanism to automatically weight the input deep features according to their importance in domain classification. With this attentive re-weighting, ADDIT can focus on the domain normalization of phonetic components that are more susceptible to domain variability and generates deep features with improved domain-invariance and senone-discriminativity over ADIT. Most importantly, the attention block serves only as an external component to the DNN acoustic model and is not involved in ASR, so AADIT can be used to improve the acoustic modeling with any DNN architectures. More generally, the same methodology can improve any adversarial learning system with an auxiliary discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3% relative WER improvements, respectively, over a multi-conditional model and a strong ADIT baseline.

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Paper Details

Authors:
Zhong Meng, Jinyu Li, Yifan Gong
Submitted On:
12 May 2019 - 9:03pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Yifan Gong
Paper Code:
3451
Document Year:
2019
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Document Files

aadit_poster.pptx

(19)

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[1] Zhong Meng, Jinyu Li, Yifan Gong, "Attentive Adversarial Learning for Domain-Invariant Training", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4474. Accessed: Jul. 19, 2019.
@article{4474-19,
url = {http://sigport.org/4474},
author = {Zhong Meng; Jinyu Li; Yifan Gong },
publisher = {IEEE SigPort},
title = {Attentive Adversarial Learning for Domain-Invariant Training},
year = {2019} }
TY - EJOUR
T1 - Attentive Adversarial Learning for Domain-Invariant Training
AU - Zhong Meng; Jinyu Li; Yifan Gong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4474
ER -
Zhong Meng, Jinyu Li, Yifan Gong. (2019). Attentive Adversarial Learning for Domain-Invariant Training. IEEE SigPort. http://sigport.org/4474
Zhong Meng, Jinyu Li, Yifan Gong, 2019. Attentive Adversarial Learning for Domain-Invariant Training. Available at: http://sigport.org/4474.
Zhong Meng, Jinyu Li, Yifan Gong. (2019). "Attentive Adversarial Learning for Domain-Invariant Training." Web.
1. Zhong Meng, Jinyu Li, Yifan Gong. Attentive Adversarial Learning for Domain-Invariant Training [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4474