TL;DR
This study systematically explores the contribution of CMVNC features across two deep learning models, and juxtaposes them against the traditional i-vector/PLDA baseline under varying training and testing utterance lengths.
Target Speaker Extraction (TSE) is pivotal in speech communication and human-computer interaction, enabling the isolation of a specific speaker's voice from complex acoustic environments, i.e., the cocktail party scenario. Although traditional TSE systems conditioned on enrollment speech have progressed substantially,...
A Conditional Speaker Normalization module is introduced that conditions the normalization process on speaker embeddings derived from either speaker verification systems or a speaker-height estimation model to mitigate the speaker-dependent nature of Log-Area Ratios.
Speaker verification (SV) performance degrades under language mismatch due to the entanglement of speaker identity with language-specific acoustic cues. To address this problem, we leverage large-scale self-supervised pre-trained models (PTMs) to learn language-agnostic speaker representations. We utilize PTMs as robus...
Target-speaker and multi-speaker extraction are techniques for extracting speech from a desired speaker or desired speakers in the presence of other speakers and/or noise. Neural network approaches for this task are often trained and evaluated using simulated datasets, with balanced amounts of target speech and speaker...
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