RI: Extension of the APP detector for multipitch tracking and speaker separation

在许多真实的世界场景中，语音识别和说话者识别系统必须处理来自多个说话者的同时语音，即，表示自然环境中的对话的语音混合。人工耳蜗的使用者在多扬声器环境中分离扬声器时会遇到问题，因为失去了精细的时间结构。因此，这种系统的一个关键的预处理步骤是根据其组成来源的语音分离。该项目是这个过程的第一部分，涉及识别说话者的数量和基于语音信号的周期性部分（即，有声区域）。由于不同的扬声器具有作为声带生理学的结果的特征音高范围，音高轨迹可以用于帮助将组合信号分离成不同的语音流。当前流行的多音调跟踪方法容易受到由不同语音信号的周期性区域之间的相互作用引起的伪影的影响。 因此，组合信号的周期性可以不同于各个分量的周期性。主要的新思想是现有的周期性和音高估计过程的扩展到更高的维度，到达一个多维的周期性函数，这是不容易受到谐波相互作用的文物。初步结果表明，即使一个扬声器比另一个扬声器更占主导地位，获得的多个音高轨迹是准确的。 该方法很容易推广到非语音音频，它应该是强大的噪声信道。这个项目的成果将被用于未来的项目中，其中实际的语音流将被从彼此分离的多音高信息的基础上。

In many real world scenarios, speech recognition and speaker identification systems must deal with simultaneous speech from several talkers, i.e., speech mixtures representing conversations in natural environments. Users of cochlear implants encounter problems in separating speakers in multi-speaker environments, because of the loss of fine temporal structure. Thus, a crucial preprocessing step for such systems is the segregation of speech according to its constituent sources. The project is the first part of this process which involves the recognition of the number of speakers and the separation of their pitch tracks based on the periodic portions of the speech signal (i.e., voiced regions). Since different speakers have characteristic pitch ranges as a consequence of vocal cord physiology, pitch tracks can be used to help separate the combined signal into different speech streams. Current popular multi-pitch tracking approaches are susceptible to artifacts caused by the interaction between the periodic regions of the different speech signals. Consequently, the periodicity of the combined signal can be different from that of the individual components. The major new idea is the extension of an existent periodicity and pitch estimation process to higher dimensions, arriving at a multi-dimensional periodicity function which is not susceptible to the harmonic interaction artifacts. Preliminary results show that the multiple pitch tracks obtained are accurate even when one speaker is considerably more dominant than the other speaker. The approach is easily generalized to non-speech audio and it should be robust in noisy channels. The outcome of this project will be used in a future project where the actual speech streams will be separated from each other based on the multi-pitch information.