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A Principle Solution for Enroll-Test Mismatch in Speaker Recognition
Mismatch between enrollment and test conditions causes serious performan...
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DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis
Automatic speaker recognition algorithms typically characterize speech a...
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Spot the conversation: speaker diarisation in the wild
The goal of this paper is speaker diarisation of videos collected 'in th...
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Synthesising 3D Facial Motion from "In-the-Wild" Speech
Synthesising 3D facial motion from speech is a crucial problem manifesti...
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CN-CELEB: a challenging Chinese speaker recognition dataset
Recently, researchers set an ambitious goal of conducting speaker recogn...
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Speaker detection in the wild: Lessons learned from JSALT 2019
This paper presents the problems and solutions addressed at the JSALT wo...
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Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
The effects of speaking-style variability on automatic speaker verificat...
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CN-Celeb: multi-genre speaker recognition
Research on speaker recognition is extending to address the vulnerability in the wild conditions, among which genre mismatch is perhaps the most challenging, for instance, enrollment with reading speech while testing with conversational or singing audio. This mismatch leads to complex and composite inter-session variations, both intrinsic (i.e., speaking style, physiological status) and extrinsic (i.e., recording device, background noise). Unfortunately, the few existing multi-genre corpora are not only limited in size but are also recorded under controlled conditions, which cannot support conclusive research on the multi-genre problem. In this work, we firstly publish CN-Celeb, a large-scale multi-genre corpus that includes in-the-wild speech utterances of 3,000 speakers in 11 different genres. Secondly, using this dataset, we conduct a comprehensive study on the multi-genre phenomenon, in particular the impact of the multi-genre challenge on speaker recognition, and on how to utilize the valuable multi-genre data more efficiently.
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