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Keyword spotting (KWS) and speaker verification (SV) have been studiedindependently although it is known that acoustic and speaker domains arecomplementary. In this paper, we propose a multi-task network that performs KWSand SV simultaneously to fully utilize the interrelated domain information. Themulti-task network tightly combines sub-networks aiming at performanceimprovement in challenging conditions such as noisy environments,open-vocabulary KWS, and short-duration SV, by introducing novel techniques ofconnectionist temporal classification (CTC)-based soft voice activity detection(VAD) and global query attention. Frame-level acoustic and speaker informationis integrated with phonetically originated weights so that forms a word-levelglobal representation. Then it is used for the aggregation of feature vectorsto generate discriminative embeddings. Our proposed approach shows 4.06% and26.71% relative improvements in equal error rate (EER) compared to thebaselines for both tasks. We also present a visualization example and resultsof ablation experiments.

Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention