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On Feature Importance and Interpretability of Speaker Representations
Unsupervised speech disentanglement aims at separating fast varying from slowly varying components of a speech signal.
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query.
Investigating Speaker Embedding Disentanglement on Natural Read Speech
To quantify disentanglement, we identify acoustic features that are highly speaker-variant and can serve as proxies for the factors of variation underlying speech.
Investigation into Target Speaking Rate Adaptation for Voice Conversion
Disentangling speaker and content attributes of a speech signal into separate latent representations followed by decoding the content with an exchanged speaker representation is a popular approach for voice conversion, which can be trained with non-parallel and unlabeled speech data.
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