Prosody-aware dual-stream neural model for speaker-adaptive voice command recognition

VOSSA: Voiceprint Optimization for Streaming Speech Architectures

Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effective for speaker discrimination, these embeddings are trained to remain stable across phonetic and prosodic variations within-speaker, which may conflict with frame-level a...

Conditional Speaker Normalization of Vocal Tract Shape Features for Robust Synthetic Speech Detection

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.

What Survives the Codec Shift: Pooled No-Vocals Residuals for Speech Deepfake Detection

The transition from vocoder-based to neural-codec speech synthesis makes generalization more difficult for speech deepfake detectors, particularly those relying on speech-oriented representations. It remains unclear which acoustic representations retain discriminative information when the generation mechanism changes....

An encoder–decoder transformer with signal-level data augmentation for robust speech emotion recognition

SPEAR-Gen: Generation-Aware Pre-training for Unified Speech Representations

Speech understanding and generation place different demands on speech representations, and existing models are typically optimised towards one capability or the other. To reduce this gap, we introduce SPEAR-Gen, a speech representation model that learns a single representation for both capabilities. Task-aligned featur...

Target-speaker adaptation in text-to-speech synthesis: a comparison of efficient fine-tuning and zero-shot methods

It is shown that fine-tuning a non-autoregressive architecture, such as ForwardTacotron, achieves speaker-adaptation quality comparable to zero-shot methods, with lower computational complexity but a lower mean opinion score.