【限时资源，期刊全文】Computer Speech and Language《计算机语音和语言》2020年第63卷-共14篇论文（侵删）-LingLab

【限时资源，期刊全文】Computer Speech and Language《计算机语音和语言》2020年第63卷-共14篇论文（侵删）

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应用语言学计算语言学自然语言处理

本期推送的是SCI期刊——Computer Speech and Language《计算机语音和语言》最新一期、即2020年第63卷的14篇论文，其中目录可在正文中查看，全文可以点击文末附件列表下载，下载链接即日起三天内有

Computer Speech and Language《计算机语音和语言》2020年第63卷-共14篇论文（侵删）

资料整理：张明辉（微信：zhangxiaojian160408）

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序号	类型	作者	题目
01	Article	Wen & Young	Recurrent neural network language generation for spoken dialogue systems
02	Article	Mingote et al.	Optimization of the area under the ROC curve using neural network supervectors for text-dependent speaker verification
03	Article	Shivakumar & Georgiou	Transfer learning from adult to children for speech recognition: Evaluation, analysis and recommendations
04	Article	Le et al.	Hierarchical multimodal attention for end-to-end audio-visual scene-aware dialogue response generation
05	Article	Kumar et al.	Leveraging Linguistic Context in Dyadic Interactions to Improve Automatic Speech Recognition for Children
06	Special Issue	Chettri et al.	Deep generative variational autoencoding for replay spoof detection in automatic speaker verification
07	Special Issue	Monteiro et al.	Generalized end-to-end detection of spoofing attacks to automatic speaker recognizers
08	Special Issue	Chiang et al.	Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
09	Special Issue	Boussaha et al.	End-to-end response selection based on multi-level context response matching
10	Special Issue	Ye et al.	Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model
11	Special Issue	Huang et al.	RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
12	Special Issue	Tündik	A low latency sequential model and its user-focused evaluation for automatic punctuation of ASR closed captions
13	Special Issue	Lai et al.	Multilingual stance detection in social media political debates
14	Special Issue	Matějka	13 years of speaker recognition research at BUT, with longitudinal analysis of NIST SRE