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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.