A speaker-embedding-free enhancement model is extended to do both conventional denoising and target-speaker extraction with a zero-enrollment trick, plus a consistency loss that pairs two enrollment utterances of the same speaker to improve robustness.
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
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abstract
This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and text-independent (TI) speaker verification (SV) in a short duration scenario. The proposed challenge evaluates SdSV with varying degree of phonetic overlap between the enrollment and test utterances (cross-lingual). It is the first challenge with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker recognition. We expect that modern methods (deep neural networks in particular) will play a key role.
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Unified Architecture and Unsupervised Speech Disentanglement for Speaker Embedding-Free Enrollment in Personalized Speech Enhancement
A speaker-embedding-free enhancement model is extended to do both conventional denoising and target-speaker extraction with a zero-enrollment trick, plus a consistency loss that pairs two enrollment utterances of the same speaker to improve robustness.