Verifying if two audio segments belong to the same speaker has been recently put forward as a flexible way to carry out speaker identification, since it does not require to be re-trained when new speakers appear on the auditory scene. Although many of the current techniques have achieved high performances, they require a considerably high amount of memory, and a specific minimum length for their input audio segments. These requirements limit the applicability of these techniques in scenarios such as service robots, internet of things and virtual assistants, where computational resources are limited and the users tend to speak in short segments. In this work we propose a BLSTM-based model that reaches a level of performance comparable to the current state of the art when using short input audio segments, while requiring a considerably less amount of memory. Further, as far as we know, a complete speaker identification system has not been reported using this verification paradigm. Thus, we present a complete online speaker identifier, based on a simple voting system, that shows that the proposed BLSTM-based model achieves a similar performance at identifying speakers online compared to the current state of the art.

最近已经提出了验证两个音频段是否属于同一说话者的方法，作为进行说话者识别的一种灵活方法，因为当新的说话者出现在听觉场景上时不需要重新训练它。尽管许多当前技术已经实现了高性能，但是它们需要相当大的内存量，并且它们的输入音频段需要特定的最小长度。这些要求限制了这些技术在诸如服务机器人，物联网和虚拟助手之类的场景中的适用性，在这些场景中计算资源有限并且用户往往会在很短的时间内讲话。在这项工作中，我们提出了一个基于BLSTM的模型，当使用短输入音频片段时，该模型的性能水平可与当前技术水平媲美，同时需要更少的内存。此外，据我们所知，尚未使用该验证范例报告完整的说话人识别系统。因此，我们基于一个简单的投票系统提供了一个完整的在线讲话者标识符，表明与现有技术相比，该基于BLSTM的模型在识别在线讲话者方面实现了类似的性能。