术语“频率”的详细信息

英译术语:

frequency

汉语语境:

通过变化汉语合成词的词素累积频率的方法，考察表面频率和语义透明度对加工速度的影响。研究发现，当控制透明度时，高表面频率词中存在词累累积频率效应，即词素频率越高反应越快；而在低表面频率词中没有。当控制表面频率、变化词素频率和透明度时，在透明词中，词素累积频率高的比低的加工快；但在不透明词中，这种反应趋势反转了：词素频率越高反应越慢。这项研究表明词和词素单元表征于同一层次，都从下一层次得到激活，且在词和词素之间有连接；在透明词中这些连接是正性的，在不透明词中这些连接是负性的。

英语语境:

The present experiments focused on the structure of access representation. Surface frequencies, cumulative morpheme frequencies, and semantic transparencies of Chinese two-character words were controlled. When transparencies were balanced, the cumulative morpheme frequency effect was found only in high surface frequency words but not in low frequency words. In the next experiment surface frequencies were controlled and cumulative morpheme frequencies and transparencies were verified. The responses of words with high cumulative frequencies were faster than that with low cumulative frequencies in transparent words. In opaque words, however, this response tendency was reversed; namely, the responses of words with high cumulative frequencies were slower than that with low cumulative frequencies. The two experiments implied that both word and morpheme units received activation from visual input and there were connections between them. These connections were positive between transparent words and their morphemes, negative between opaque words and their morphemes, and would be enhanced with surface frequencies.

英译术语:

frequence

汉语语境:

针对Mel频率倒谱系数仅反映语音静态特征导致的方言识别率低的问题，而滑动差分倒谱由于考虑了前后帧差分倒谱的影响，能反映语音的动态特征；同时考虑方言的静态与动态特征，对普通话、上海话、广东话和闽南话4种方言进行Mel频率倒谱系数特征和滑动差分倒谱特征提取，将其两组特征组合送入支持向量机进行辨识，并研究了针对4种方言的滑动差分倒谱的局部最优参数组合。仿真实验结果表明，同时考虑方言的静态与动态特征方法的识别率高达百分之92.5，但识别率的提高是以延长运算时间为代价的。

英语语境:

MFCC（Mel Frequence Cepstral Coefficients）only reflects speech static feature so its dialect recognition rate is low, while SDC（Shifted Delta Cepstra）reflects speech dynamic feature because of considering the connections between several speech frames. For combination of static and dynamic features, MFCC and SDC extracted from Mandarin, Shanghai dialect, Cantonese, Minnan dialect are employed as the feature vector with SVM （Support Vector Machine）for the dialect identification, and effects on performance of different parameters for SDC are stud- ied. Simulation results demonstrate that the dialect recognition rate with static and dynamic features can be up to 92. 5 percent, but its increase is based on the cost of the working time.