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Found 10 papers, 2 papers with code
Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing
The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited.
Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024
This work details our approach to achieving a leading system with a 1. 79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (CtrSVDD).
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection
Self-supervised learning (SSL) speech representation models, trained on large speech corpora, have demonstrated effectiveness in extracting hierarchical speech embeddings through multiple transformer layers.
How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?
Recent work shows that countermeasures (CMs) trained on partially spoofed audio can effectively detect such spoofing.
Voice Conversion Augmentation for Speaker Recognition on Defective Datasets
Our experimental results on three created datasets demonstrated that VCA-NN effectively mitigates these dataset problems, which provides a new direction for handling the speaker recognition problems from the data aspect.
Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification
We represent the stride space on a trellis diagram, and conduct a systematic study on the impact of temporal and frequency resolutions on the performance and further identify two optimal points, namely Golden Gemini, which serves as a guiding principle for designing 2D ResNet-based speaker verification models.
Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
We propose a log-likelihood ratio function for the PLDA scoring with the uncertainty propagation.
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?
The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler cosine similarity measure for speaker verification.
MFA: TDNN with Multi-scale Frequency-channel Attention for Text-independent Speaker Verification with Short Utterances
The time delay neural network (TDNN) represents one of the state-of-the-art of neural solutions to text-independent speaker verification.
Speaker-Utterance Dual Attention for Speaker and Utterance Verification
The proposed SUDA features an attention mask mechanism to learn the interaction between the speaker and utterance information streams.
