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

Myunghun Jung; Youngmoon Jung; Jahyun Goo; Hoirin Kim

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arXiv · 2005.03867

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

Myunghun Jung·Youngmoon Jung·Jahyun Goo·Hoirin Kim

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Abstract

Keyword spotting (KWS) and speaker verification (SV) have been studied independently although it is known that acoustic and speaker domains are complementary. In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information. The multi-task network tightly combines sub-networks aiming at performance improvement in challenging conditions such as noisy environments, open-vocabulary KWS, and short-duration SV, by introducing novel techniques of connectionist temporal classification (CTC)-based soft voice activity detection (VAD) and global query attention. Frame-level acoustic and speaker information is integrated with phonetically originated weights so that forms a word-level global representation. Then it is used for the aggregation of feature vectors to generate discriminative embeddings. Our proposed approach shows 4.06% and 26.71% relative improvements in equal error rate (EER) compared to the baselines for both tasks. We also present a visualization example and results of ablation experiments.

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Myunghun Jung, Youngmoon Jung, Jahyun Goo, Hoirin Kim. 2020-05-08. Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention. https://arxiv.org/abs/2005.03867

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