Learnable Spectro-temporal Receptive Fields for Robust Voice Type Discrimination

Autor:	Vuong, Tyler, Xia, Yangyang, Stern, Richard
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.21437/Interspeech.2020-1878
Popis:	Voice Type Discrimination (VTD) refers to discrimination between regions in a recording where speech was produced by speakers that are physically within proximity of the recording device ("Live Speech") from speech and other types of audio that were played back such as traffic noise and television broadcasts ("Distractor Audio"). In this work, we propose a deep-learning-based VTD system that features an initial layer of learnable spectro-temporal receptive fields (STRFs). Our approach is also shown to provide very strong performance on a similar spoofing detection task in the ASVspoof 2019 challenge. We evaluate our approach on a new standardized VTD database that was collected to support research in this area. In particular, we study the effect of using learnable STRFs compared to static STRFs or unconstrained kernels. We also show that our system consistently improves a competitive baseline system across a wide range of signal-to-noise ratios on spoofing detection in the presence of VTD distractor noise. Comment: Accepted Interspeech 2020. Video: http://www.interspeech2020.org/index.php?m=content&c=index&a=show&catid=311&id=712
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.09151 Zobrazit plný text záznamu View this record from Arxiv