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Speaker Embedding Augmentation with Noise Distribution Matching.

Revisiting the Statistics Pooling Layer in Deep Speaker Embedding Learning

SELF-SUPERVISED LEARNING BASED DOMAIN ADAPTATION FOR ROBUST SPEAKER VERIFICATION

SYNAUG:SYNTHESIS-BASED DATA AUGMENTATION FOR TEXT-DEPENDENT SPEAKER VERIFICATION

Unit Selection Synthesis based Data Augmentation for Fixed Phrase Speaker Verification

Analysis of ABC Submission to NIST SRE 2019 CMN and VAST Challenge

We present a condensed description and analysis of the joint submission of ABC team for NIST SRE 2019, by BUT, CRIM, Phonexia, Omilia and UAM. We concentrate on challenges that arose during development and we analyze the results obtained on the …

But System for the Second Dihard Speech Diarization Challenge

This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge, with source code available

Optimizing Bayesian HMM based x-vector clustering for the second DIHARD speech diarization challenge

This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input …

Text Adaptation for Speaker Verification with Speaker-Text Factorized Embeddings

We proposed the text-adptation speaker verification task and an intital solution called Speaker-text factorization network, which could deal with different text-mismatch conditions

Channel Invariant Speaker Embedding Learning with Joint Multi-Task and Adversarial Training

Using deep neural network to extract speaker embedding has significantly improved the speaker verification task. However, such embeddings are still vulnerable to channel variability. Previous works have used adversarial training to suppress channel …