DNN-Based Voice Activity Detection with Multi-Task Learning

• KANG Tae Gyoon

  Department of Electrical and Computer Engineering and the Institute of New Media and Communications, Seoul National University

• KIM Nam Soo

  Department of Electrical and Computer Engineering and the Institute of New Media and Communications, Seoul National University

抄録

Recently, notable improvements in voice activity detection (VAD) problem have been achieved by adopting several machine learning techniques. Among them, the deep neural network (DNN) which learns the mapping between the noisy speech features and the corresponding voice activity status with its deep hidden structure has been one of the most popular techniques. In this letter, we propose a novel approach which enhances the robustness of DNN in mismatched noise conditions with multi-task learning (MTL) framework. In the proposed algorithm, a feature enhancement task for speech features is jointly trained with the conventional VAD task. The experimental results show that the DNN with the proposed framework outperforms the conventional DNN-based VAD algorithm.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E99.D (2), 550-553, 2016

  一般社団法人 電子情報通信学会

キーワード

• deep neural network
• voice activity detection
• multi-task learning

詳細情報

• CRID: 1390282679355434752
• NII論文ID: 130005121994
• DOI: 10.1587/transinf.2015edl8168
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E99.D/2/E99.D_2015EDL8168/_pdf
• 本文言語コード: en
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