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MLNET An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection

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Document pages: 5 pages

Abstract: Voice activity detection (VAD) makes a distinction between speech andnon-speech and its performance is of crucial importance for speech basedservices. Recently, deep neural network (DNN)-based VADs have achieved betterperformance than conventional signal processing methods. The existed DNNbasedmodels always handcrafted a fixed window to make use of the contextual speechinformation to improve the performance of VAD. However, the fixed window ofcontextual speech information can t handle various unpredicatable noiseenvironments and highlight the critical speech information to VAD task. Inorder to solve this problem, this paper proposed an adaptive multiplereceptive-field attention neural network, called MLNET, to finish VAD task. TheMLNET leveraged multi-branches to extract multiple contextual speechinformation and investigated an effective attention block to weight the mostcrucial parts of the context for final classification. Experiments inreal-world scenarios demonstrated that the proposed MLNET-based modeloutperformed other baselines.

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