5 papers
A Parameter-Efficient Multi-Scale Convolutional Adapter for Synthetic Speech Detection
Yassine El Kheir, Fabian Ritter-Guttierez, Arnab Das +2
Recent synthetic speech detection models typically adapt a pre-trained SSL model via finetuning, which is computationally demanding. Parameter-Efficient Fine-Tuning (PEFT) offers a…
Two Views, One Truth: Spectral and Self-Supervised Features Fusion for Robust Speech Deepfake Detection
Yassine El Kheir, Arnab Das, Enes Erdem Erdogan +3
Recent advances in synthetic speech have made audio deepfakes increasingly realistic, posing significant security risks. Existing detection methods that rely on a single modality,…
Dataset-Distillation Generative Model for Speech Emotion Recognition
Fabian Ritter-Gutierrez, Kuan-Po Huang, Jeremy H. M Wong +4
Deep learning models for speech rely on large datasets, presenting computational challenges. Yet, performance hinges on training data size. Dataset Distillation (DD) aims to learn…
Noise robust distillation of self-supervised speech models via correlation metrics
Fabian Ritter-Gutierrez, Kuan-Po Huang, Dianwen Ng +4
Compared to large speech foundation models, small distilled models exhibit degraded noise robustness. The student's robustness can be improved by introducing noise at the inputs du…
Improving generalizability of distilled self-supervised speech processing models under distorted settings
Kuan-Po Huang, Yu-Kuan Fu, Tsu-Yuan Hsu +5
Self-supervised learned (SSL) speech pre-trained models perform well across various speech processing tasks. Distilled versions of SSL models have been developed to match the needs…