近年来,非负矩阵分解(non-negative matrix factorization,NMF)被广泛应用于单通道语音分离问题。然而,标准的NMF算法假设语音的相邻帧之间是相互独立的,不能表征语音信号的时间连续性信息。为此,该文提出了一种基于NMF和因子条件随机场(factorial conditional random field,FCRF)的语音分离算法,首先将NMF和k均值聚类结合对纯净语音的频谱结构以及时间连续性进行建模,然后利用得到的模型训练FCRF模型,进而对混合语音信号进行分离。结果表明:该算法相比没有考虑语音时间连续特性的基于NMF的算法如激活集牛顿算法(active-set Newton algorithm,ASNA),在客观指标上有明显提高。
Based on the Motor Theory of speech perception, the interaction between the auditory and motor systems plays an essential role in speech perception. Since the Motor Theory was proposed, it has received remarkable attention in the field. However, each of the three hypotheses of the theory still needs further verification. In this review, we focus on how the auditory-motor anatomical and functional associations play a role in speech perception and discuss why previous studies could not reach an agreement and particularly whether the motor system involvement in speech perception is task-load dependent. Finally, we suggest that the function of the auditory-motor link is particularly useful for speech perception under adverse listening conditions and the further revised Motor Theory is a potential solution to the ＂cocktail-party＂ problem.
