Single channel speech enhancement via time-frequency dictionary learning

A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.

汉语母语者对英语对立辅音的习得能力研究

目前,研究者们就母语与目的语之间语音系统和音位特征的同化与区分进行了许多研究。本研究旨在研究以英语为二语的中文学习者(English Learner of Chinese,ELC)对三对英语辅音对(/w/-/r/;/s/-/z/;/f/-/v/)的区分程度,分别从语音和音系学的角度测试语音学习模型(Speech Learning Model,SLM),并尝试观察实验者对三对英语辅音对的感知模式。结果表明:1.在三对辅音中/r/,/f/和/s/声音更容易从对应的/w/,/v/和/z/分辨出来;2.选中的辅音对在词汇中出现的位置会影响声音的感知;3.在声音感知方面,语言水平和语音系统的差异共同作用于被试者对三对辅音对的习得。感知任务中的不同表现说明,母语与二语中相似和不相似的语音同样重要,这意味着在二语学习中,学习者们应该了解二语语音系统的整体情况,而不是只努力去纠正二语习得中与母语差异大的语音。