Search Results for author:
Found 8 papers, 5 papers with code
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision
Using SPARO, we demonstrate improvements on downstream recognition, robustness, retrieval, and compositionality benchmarks with CLIP (up to +14% for ImageNet, +4% for SugarCrepe), and on nearest neighbors and linear probe for ImageNet with DINO (+3% each).
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning
Self-supervised learning (SSL) has achieved remarkable success across various speech-processing tasks.
Towards Robust FastSpeech 2 by Modelling Residual Multimodality
State-of-the-art non-autoregressive text-to-speech (TTS) models based on FastSpeech 2 can efficiently synthesise high-fidelity and natural speech.
Efficient Training of Deep Equilibrium Models
Deep equilibrium models (DEQs) have proven to be very powerful for learning data representations.
AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling
Using this differentiable duration method, we introduce AutoTTS, a direct text-to-waveform speech synthesis model.
Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition
Recent works have revealed the vulnerability of automatic speech recognition (ASR) models to adversarial examples (AEs), i. e., small perturbations that cause an error in the transcription of the audio signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
NVC-Net: End-to-End Adversarial Voice Conversion
By disentangling the speaker identity from the speech content, NVC-Net is able to perform non-parallel traditional many-to-many voice conversion as well as zero-shot voice conversion from a short utterance of an unseen target speaker.
A Simple Approach for Zero-Shot Learning based on Triplet Distribution Embeddings
Given the semantic descriptions of classes, Zero-Shot Learning (ZSL) aims to recognize unseen classes without labeled training data by exploiting semantic information, which contains knowledge between seen and unseen classes.
