第ä����å±��碩士è«𡝗��𤾸��𤾸���
�𡁜£«è«𡝗��𠬍�
�ªç��𦒘��㵪��²ç��睲��¬å��羓���
å¾㛖�å§枏�ï¼����è±�(æ¸�虾大å¸�»æ�å·¥ç��𠉛©¶��)
ä¸æ�é¡𣬚𤌍ï¼��ºæ䲰循ç¢Zå¼誩��埈�§ç¶²è·¯ç��¸æ�å¢鮋��𦠜¢¯åº¦å�覺ç��㚚俈禦æ��¶å»ºç«见¼·�¥å�èª鮋𨺗���辨è�模å�
�±æ�é¡𣬚𤌍ï¼�Building a Robust Speech Emotion Recognition using Cycle-GAN-based Data Augmentation and Gradient-Aware Purification Adversarial Defense Mechanism
����蹱�ï¼𡁏�ç¥�� �蹱�
��
ä½³ä��𦒘��㵪��²ç��睲��¬å��羓���
å¾㛖�å§枏�ï¼�æ´ªå���(�𣂼�大å¸å¤𡁜�é«𠉛³»çµ±è��ºæ��钅�ç®堒·¥ç¨见�士å¸ä½滚¸ç¨�)
ä¸æ�é¡𣬚𤌍ï¼�å¼·å��§è��³å��¥å¸ç¿坿�èª鮋𨺗è³���¸ä�ä½𦦵鍂�¼è����è劐��𠉛©¶
�±æ�é¡𣬚𤌍ï¼�A Study on Robust Speaker Embedding Learning and Phonetic Information Interaction for Speaker Verification
����蹱�ï¼𡁜閦å®埈��蹱�����°æ� �蹱�
��
碩士è«𡝗��𠬍�
�ªç��𦒘��㵪��²ç��睲��¬å��羓���
å¾㛖�å§枏�ï¼�æ¥羓�ä»�(�½æ�交é�𡁜¤§å¸é𤓖æ©��)
ä¸æ�é¡𣬚𤌍ï¼����å¸ç��㗛�調é��¨æ��¨æ䲰�躰�ç¥䂿�編解碼å膥èª鮋𨺗���
�±æ�é¡𣬚𤌍ï¼�Continual Gated Adapter for Bilingual Neural Codec Text-to-Speech
����蹱�ï¼𡁶°¡ä»�� �蹱�
ä½³ä��𦒘��㵪��²ç��睲�ä»笔��羓���
1. å¾㛖�å§枏�ï¼��匧���(�½æ�交é�𡁜¤§å¸è��𡁜��¨ç¢©å£«å¸ä½滚¸ç¨�)
ä¸æ�é¡𣬚𤌍ï¼��𥪜�å¼誩�æ¯𥪜¸ç¿埝䲰��¨�睃��©æ�
�±æ�é¡𣬚𤌍ï¼�Collaborative Contrastive Learning for Hypothesis Domain Adaptation
����蹱�ï¼�ç°¡ä�å®�
�蹱�
2. å¾㛖�å§枏�ï¼�æ¥𠰴���(�ºç�大å¸è³��ç§穃¸å¸ä�å¸ç�)
ä¸æ�é¡𣬚𤌍ï¼�å¤𡝗�å¼讛�è¨�模å�: �©ç鍂ä¸��讠°¡�®ç�è¿´æ¸æ¨¡å��§å���𧋦���
�±æ�é¡𣬚𤌍ï¼�Plug-in Language Model: Controlling Text Generation with a Simple Regression Model
����蹱�ï¼�馬å��� �蹱�����𨅯£¬ �蹱�
3. å¾㛖�å§枏�ï¼��堒³»æ¯�(�𣂼�大å¸äººå·¥�ºæ�ç§烐�碩士å¸ä�å¸ç�)
ä¸æ�é¡𣬚𤌍ï¼��©ç鍂å¸ç�èª𧼮蘂表é��¹é�²ä¸��𪃾è©𧼮��¼ä��峕�æº𣇉��©æ���
�±æ�é¡𣬚𤌍ï¼�Improving Multi-Criteria Chinese Word Segmentation through Learning Sentence Representation
����蹱�ï¼�é«睃�å®� �蹱�