A Memory Augmented Architecture for Continuous Speaker Identification in Meetings

We introduce and analyze a novel approach to the problem of speaker identification in multi-party recorded meetings. Given a speech segment and a set of available candidate profiles, a data-driven approach is proposed learning the distance relations between them, aiming at identifying the correct speaker label corresponding to that segment. A recurrent, memory-based architecture is employed, since this class of neural networks has been shown to yield improved performance in problems requiring relational reasoning. The proposed encoding of distance relations is shown to outperform traditional distance metrics, such as the cosine distance. Additional improvements are reported when the temporal continuity of the audio signals and the speaker changes is modeled in. In this paper, the proposed method is evaluated in two different tasks, i.e. scripted and real-world business meeting scenarios, where a relative reduction in speaker error rate of 39.28% and 51.84%, respectively, is reported when compared with the baseline.