Embedding Aggregation for Far-Field Speaker Verification with Distributed Microphone Arrays

With the successful application of deep speaker embedding networks, the performance of speaker verification systems has significantly improved under clean and close-talking settings; however, unsatisfactory performance persists under noisy and far-field environments. This study aims at improving the performance of far-field speaker verification systems with distributed microphone arrays in the smart home scenario. The proposed learning framework consists of two modules: a deep speaker embedding module and an aggregation module. The former extracts a speaker embedding for each recording. The latter, based on either averaged pooling or attentive pooling, aggregates speaker embeddings and learns a unified representation for all recordings captured by distributed microphone arrays. The two modules are trained in an end-to-end manner. To evaluate this framework, we conduct experiments on the real text-dependent far-field datasets Hi Mia. Results show that our framework outperforms the naive averaged aggregation methods by 20% in terms of equal error rate (EER) with six distributed microphone arrays. Also, we find that the attention-based aggregation advocates high-quality recordings and repels low-quality ones.