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Speaker diarization is the process of labeling different speakers in a speechsignal. Deep speaker embeddings are generally extracted from short speechsegments and clustered to determine the segments belong to same speakeridentity. The x-vector, which embeds segment-level speaker characteristics bystatistically pooling frame-level representations, is one of the most widelyused deep speaker embeddings in speaker diarization. Multi-scale aggregation,which employs multi-scale representations from different layers, has recentlysuccessfully been used in short duration speaker verification. In this paper,we investigate a multi-scale aggregation approach in an x-vector embeddingframework for speaker diarization by exploiting multiple statistics poolinglayers from different frame-level layers. Thus, it is expected that x-vectorswith multi-scale aggregation have the potential to capture meaningful speakercharacteristics from short segments, effectively taking advantage of differentinformation at multiple layers. Experimental evaluation on the CALLHOME datasetshowed that our approach provides substantial improvement over the baselinex-vectors.

X-Vectors with Multi-Scale Aggregation for Speaker Diarization