{"id":123,"date":"2024-02-15T14:55:20","date_gmt":"2024-02-15T15:55:20","guid":{"rendered":"https:\/\/express24.ir\/d\/?post_type=product&p=123"},"modified":"2024-02-25T18:23:10","modified_gmt":"2024-02-25T19:23:10","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d9%87%d8%a8%d9%88%d8%af-%d8%aa%d8%b4%d8%ae%db%8c%d8%b5-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3-%da%af%d9%81%d8%aa%d8%a7%d8%b1-%d9%85%d8%b3%d8%aa%d9%82%d9%84-%d8%a7%d8%b2","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d8%a8%d9%87%d8%a8%d9%88%d8%af-%d8%aa%d8%b4%d8%ae%db%8c%d8%b5-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3-%da%af%d9%81%d8%aa%d8%a7%d8%b1-%d9%85%d8%b3%d8%aa%d9%82%d9%84-%d8%a7%d8%b2\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u0628\u0647\u0628\u0648\u062f \u062a\u0634\u062e\u06cc\u0635 \u0627\u062d\u0633\u0627\u0633 \u06af\u0641\u062a\u0627\u0631 \u0645\u0633\u062a\u0642\u0644 \u0627\u0632 \u0633\u062e\u0646\u0631\u0627\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0633\u0627\u0632\u06af\u0627\u0631\u06cc \u062a\u0648\u0632\u06cc\u0639 \u0645\u0634\u062a\u0631\u06a9 \u067e\u0648\u06cc\u0627"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nImproving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0628\u0647\u0628\u0648\u062f \u062a\u0634\u062e\u06cc\u0635 \u0627\u062d\u0633\u0627\u0633 \u06af\u0641\u062a\u0627\u0631 \u0645\u0633\u062a\u0642\u0644 \u0627\u0632 \u0633\u062e\u0646\u0631\u0627\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0633\u0627\u0632\u06af\u0627\u0631\u06cc \u062a\u0648\u0632\u06cc\u0639 \u0645\u0634\u062a\u0631\u06a9 \u067e\u0648\u06cc\u0627<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nCheng Lu, Yuan Zong, Hailun Lian, Yan Zhao, Bj\u00f6rn Schuller, Wenming Zheng<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\nPDF<\/td>\n<\/tr>\n
\u0686\u06a9\u06cc\u062f\u0647 <\/td>\nIn speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers. Consequently, when the trained model is confronted with data from new speakers, its performance tends to degrade. To address the issue, we propose a Dynamic Joint Distribution Adaptation (DJDA) method under the framework of multi-source domain adaptation. DJDA firstly utilizes joint distribution adaptation (JDA), involving marginal distribution adaptation (MDA) and conditional distribution adaptation (CDA), to more precisely measure the multi-domain distribution shifts caused by different speakers. This helps eliminate speaker bias in emotion features, allowing for learning discriminative and speaker-invariant speech emotion features from coarse-level to fine-level. Furthermore, we quantify the adaptation contributions of MDA and CDA within JDA by using a dynamic balance factor based on $\\mathcal{A}$-Distance, promoting to effectively handle the unknown distributions encountered in data from new speakers. Experimental results demonstrate the superior performance of our DJDA as compared to other state-of-the-art (SOTA) methods.<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n5<\/td>\n<\/tr>\n
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\u0627\u0632 \u0628\u0644\u0646\u062f\u06af\u0648\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.\u0627\u06cc\u0646 \u0627\u0645\u0631 \u0628\u0647 \u0627\u0632 \u0628\u06cc\u0646 \u0628\u0631\u062f\u0646 \u062a\u0639\u0635\u0628 \u0628\u0644\u0646\u062f\u06af\u0648 \u062f\u0631 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f \u060c \u0648 \u0627\u06cc\u0646 \u0627\u0645\u06a9\u0627\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u06af\u0641\u062a\u0627\u0631 \u062a\u0628\u0639\u06cc\u0636 \u0622\u0645\u06cc\u0632 \u0648 \u0645\u062a\u063a\u06cc\u0631 \u0628\u0644\u0646\u062f\u06af\u0648 \u0627\u0632 \u0633\u0637\u062d \u062f\u0631\u0634\u062a \u062a\u0627 \u0633\u0637\u062d \u0631\u06cc\u0632 \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c \u0645\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0641\u0627\u06a9\u062a\u0648\u0631 \u062a\u0639\u0627\u062f\u0644 \u067e\u0648\u06cc\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 $ \\ mathcal {a} $ \u0641\u0627\u0635\u0644\u0647 \u060c \u06a9\u0645\u06a9 \u0647\u0627\u06cc \u0633\u0627\u0632\u06af\u0627\u0631\u06cc MDA \u0648 CDA \u0631\u0627 \u062f\u0631 JDA \u062a\u0639\u06cc\u06cc\u0646 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u060c \u0648 \u062a\u0631\u0648\u06cc\u062c \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u0624\u062b\u0631 \u062a\u0648\u0632\u06cc\u0639 \u0647\u0627\u06cc \u0646\u0627\u0634\u0646\u0627\u062e\u062a\u0647 \u0627\u06cc \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0644\u0646\u062f\u06af\u0648\u0647\u0627\u06cc \u062c\u062f\u06cc\u062f \u0645\u0634\u0627\u0647\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u060c \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645.\u0646\u062a\u0627\u06cc\u062c \u062a\u062c\u0631\u0628\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0631\u062a\u0631 DJDA \u0645\u0627 \u0631\u0627 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u0633\u0627\u06cc\u0631 \u0631\u0648\u0634\u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 (SOTA) \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.<\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nSound,Machine Learning,Audio and Speech Processing,\u0635\u062f\u0627 \u060c \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u060c \u067e\u0631\u062f\u0627\u0632\u0634 \u0635\u0648\u062a\u06cc \u0648 \u06af\u0641\u062a\u0627\u0631 \u060c<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 18 January, 2024; originally announced January 2024. , Comments: Accepted by ICASSP 2024<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0627\u0626\u0647 \u0634\u062f\u0647 \u062f\u0631 18 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0698\u0627\u0646\u0648\u06cc\u0647 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u062a\u0648\u0633\u0637 ICASSP 2024 \u067e\u0630\u06cc\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n\t\r\n\t
\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n
\r\n \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t

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  • \r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t
  • \r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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