The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 편지는 교차 말뭉치 SER의 훈련 및 테스트 음성 신호가 다른 음성 말뭉치에 속하는 교차 말뭉치 음성 감정 인식(SER) 작업에 중점을 둡니다. 기존 알고리즘은 지식 전달을 촉진하기 위해 서로 다른 말뭉치 간의 공통 감정 정보를 효과적으로 추출할 수 없습니다. 이 어려운 문제를 해결하기 위해 교차 코퍼스 SER을 위한 새로운 CAEADA(Convolutional Auto-Encoder 및 Adversarial Domain Adaptation) 프레임워크가 제안되었습니다. 프레임워크는 먼저 특징 처리를 위한 1차원 컨볼루셔널 자동 인코더(XNUMXD-CAE)를 구성합니다. 이는 인접한 XNUMX차원 통계 특징 간의 상관 관계를 탐색할 수 있으며 특징 표현은 인코더-디코더 스타일 기반 아키텍처를 통해 향상될 수 있습니다. . 이후 ADA(Adversarial Domain Adaptation) 모듈은 도메인 판별자를 혼동하여 소스 도메인과 대상 도메인 간의 특징 분포 불일치를 완화하고, 특히 MMD(최대 평균 불일치)를 사용하여 특징 변환을 더 잘 수행합니다. 제안된 CAEADA를 평가하기 위해 EmoDB, eNTERFACE 및 CASIA 음성 말뭉치를 대상으로 광범위한 실험을 수행했으며 그 결과 제안한 방법이 다른 접근 방식보다 성능이 우수하다는 것을 보여줍니다.
Yang WANG
Henan University of Technology, Ministry of Education,Henan University of Technology
Hongliang FU
Henan University of Technology, Ministry of Education,Henan University of Technology
Huawei TAO
Henan University of Technology, Ministry of Education,Henan University of Technology
Jing YANG
Henan University of Technology, Ministry of Education,Henan University of Technology
Hongyi GE
Henan University of Technology, Ministry of Education,Henan University of Technology
Yue XIE
Nanjing Institute of Technology
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부
Yang WANG, Hongliang FU, Huawei TAO, Jing YANG, Hongyi GE, Yue XIE, "Convolutional Auto-Encoder and Adversarial Domain Adaptation for Cross-Corpus Speech Emotion Recognition" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 10, pp. 1803-1806, October 2022, doi: 10.1587/transinf.2022EDL8045.
Abstract: This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8045/_p
부
@ARTICLE{e105-d_10_1803,
author={Yang WANG, Hongliang FU, Huawei TAO, Jing YANG, Hongyi GE, Yue XIE, },
journal={IEICE TRANSACTIONS on Information},
title={Convolutional Auto-Encoder and Adversarial Domain Adaptation for Cross-Corpus Speech Emotion Recognition},
year={2022},
volume={E105-D},
number={10},
pages={1803-1806},
abstract={This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.},
keywords={},
doi={10.1587/transinf.2022EDL8045},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Convolutional Auto-Encoder and Adversarial Domain Adaptation for Cross-Corpus Speech Emotion Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1803
EP - 1806
AU - Yang WANG
AU - Hongliang FU
AU - Huawei TAO
AU - Jing YANG
AU - Hongyi GE
AU - Yue XIE
PY - 2022
DO - 10.1587/transinf.2022EDL8045
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2022
AB - This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.
ER -