CopyPaste-based parallel data, cosine similarity loss, and energy-based emotion masking reduce speaker verification EER by 19.29% relative on the Dusha emotional speech corpus.
Currently, SV systems that use low- dimensional speaker representations extracted from deep learning- based speaker encoders have become the dominant approach in this field
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SD 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Learning Emotion-Invariant Speaker Representations for Speaker Verification
CopyPaste-based parallel data, cosine similarity loss, and energy-based emotion masking reduce speaker verification EER by 19.29% relative on the Dusha emotional speech corpus.