考察在无线索、内源性线索与外源性线索时不同符号数字在注意与非注意条件下的空间-数字的反应编码联合效应(Spatial Numerical Association of Response Codes,简称SNARC效应)。采用1到9的中文与阿拉伯数字为材料,以判断数字奇偶为任...考察在无线索、内源性线索与外源性线索时不同符号数字在注意与非注意条件下的空间-数字的反应编码联合效应(Spatial Numerical Association of Response Codes,简称SNARC效应)。采用1到9的中文与阿拉伯数字为材料,以判断数字奇偶为任务。实验结果表明:(1)无线索时注意条件下阿拉伯和中文数字都出现了SNARC效应,而非注意条件下则都没有出现,并且受影响的主要是较大的数字(8、9);(2)外源性线索和内源性线索时,我们得到一个逐渐递减的SNARC效应,受影响的也主要是较大的数字(8、9)。在内源性线索的注意条件阿拉伯和中文数都有SNARC效应,而在非注意条件只有阿拉伯数字有SNARC效应;在外源性线索的注意条件只有阿拉伯数字有SNARC效应,而在非注意条件阿拉伯和中文都没有SNARC效应,说明外源性注意的影响比内源性注意更大,中文数字所受的影响比阿拉伯数字更大。展开更多
In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) a...In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) and convolutional neural networks(CNNs) that can effectively exploit variablelength contextual information,and their various combination with other models.We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system,the connectionist temporal classification(CTC) criterion,and the attention-based sequenceto-sequence translation model.We further illustrate robustness issues in speech recognition systems,and discuss acoustic model adaptation,speech enhancement and separation,and robust training strategies.We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.展开更多
文摘考察在无线索、内源性线索与外源性线索时不同符号数字在注意与非注意条件下的空间-数字的反应编码联合效应(Spatial Numerical Association of Response Codes,简称SNARC效应)。采用1到9的中文与阿拉伯数字为材料,以判断数字奇偶为任务。实验结果表明:(1)无线索时注意条件下阿拉伯和中文数字都出现了SNARC效应,而非注意条件下则都没有出现,并且受影响的主要是较大的数字(8、9);(2)外源性线索和内源性线索时,我们得到一个逐渐递减的SNARC效应,受影响的也主要是较大的数字(8、9)。在内源性线索的注意条件阿拉伯和中文数都有SNARC效应,而在非注意条件只有阿拉伯数字有SNARC效应;在外源性线索的注意条件只有阿拉伯数字有SNARC效应,而在非注意条件阿拉伯和中文都没有SNARC效应,说明外源性注意的影响比内源性注意更大,中文数字所受的影响比阿拉伯数字更大。
文摘In this paper,we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques.We first discuss models such as recurrent neural networks(RNNs) and convolutional neural networks(CNNs) that can effectively exploit variablelength contextual information,and their various combination with other models.We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system,the connectionist temporal classification(CTC) criterion,and the attention-based sequenceto-sequence translation model.We further illustrate robustness issues in speech recognition systems,and discuss acoustic model adaptation,speech enhancement and separation,and robust training strategies.We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.
