11 citations · 24 across the 21 of their papers we have counts for
21 papers
SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization
Yuan Tseng, Aishwarya Fursule, Andrew Zijun Ma +4
Recent improvements in voice-cloning speech generation systems raise concerns about misuse by malicious actors to impersonate others and spread misinformation. Detecting such tampe…
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
Aishwarya R. Fursule, Vamshi Nallaguntla, Shruti Kshirsagar +1
Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demogra…
Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains critically underexamined. Prio…
Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings
Vamshi Nallaguntla, Shruti Kshirsagar, Anderson R. Avila
Recent advances in emotional voice conversion (EVC) have enabled the generation of expressive synthetic speech, raising new concerns in audio deepfake detection. Existing approache…
PhonemeDF: A Synthetic Speech Dataset for Audio Deepfake Detection and Naturalness Evaluation
Vamshi Nallaguntla, Aishwarya Fursule, Shruti Kshirsagar +1
The growing sophistication of speech generated by Artificial Intelligence (AI) has introduced new challenges in audio deepfake detection. Text-to-speech (TTS) and voice conversion…
Investigating the Impact of Speech Enhancement on Audio Deepfake Detection in Noisy Environments
Anacin, Angela, Shruti Kshirsagar +1
Logical Access (LA) attacks, also known as audio deepfake attacks, use Text-to-Speech (TTS) or Voice Conversion (VC) methods to generate spoofed speech data. This can represent a s…
Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
Aishwarya Fursule, Shruti Kshirsagar, Anderson R. Avila
Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biomet…
BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications
Heitor R. Guimarães, Abhishek Tiwari, Mahsa Abdollahi +2
Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable contin…
An Efficient End-to-End Approach to Noise Invariant Speech Features via Multi-Task Learning
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +3
Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstr…
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''
Arthur Pimentel, Heitor Guimarães, Anderson R. Avila +2
Recent advances with self-supervised learning have allowed speech recognition systems to achieve state-of-the-art (SOTA) word error rates (WER) while requiring only a fraction of t…
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks
Heitor R. Guimarães, Arthur Pimentel, Anderson Avila +1
Keyword spotting (KWS) refers to the task of identifying a set of predefined words in audio streams. With the advances seen recently with deep neural networks, it has become a popu…
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding
Anderson R. Avila, Mehdi Rezagholizadeh, Chao Xing
Recent voice assistants are usually based on the cascade spoken language understanding (SLU) solution, which consists of an automatic speech recognition (ASR) engine and a natural…
On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications
Vamsikrishna Chemudupati, Marzieh Tahaei, Heitor Guimaraes +5
Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to unive…
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications
Heitor Guimarães, Arthur Pimentel, Anderson Avila +2
Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw…
How Secure is Code Generated by ChatGPT?
Raphaël Khoury, Anderson R. Avila, Jacob Brunelle +1
In recent years, large language models have been responsible for great advances in the field of artificial intelligence (AI). ChatGPT in particular, an AI chatbot developed and rec…
RobustDistiller: Compressing Universal Speech Representations for Enhanced Environment Robustness
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +3
Self-supervised speech pre-training enables deep neural network models to capture meaningful and disentangled factors from raw waveform signals. The learned universal speech repres…
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement
Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila +2
Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech an…
Low-bit Shift Network for End-to-End Spoken Language Understanding
Anderson R. Avila, Khalil Bibi, Rui Heng Yang +3
Deep neural networks (DNN) have achieved impressive success in multiple domains. Over the years, the accuracy of these models has increased with the proliferation of deeper and mor…
Sequential End-to-End Intent and Slot Label Classification and Localization
Yiran Cao, Nihal Potdar, Anderson R. Avila
Human-computer interaction (HCI) is significantly impacted by delayed responses from a spoken dialogue system. Hence, end-to-end (e2e) spoken language understanding (SLU) solutions…
A Streaming End-to-End Framework For Spoken Language Understanding
Nihal Potdar, Anderson R. Avila, Chao Xing +3
End-to-end spoken language understanding (SLU) has recently attracted increasing interest. Compared to the conventional tandem-based approach that combines speech recognition and l…
Non-intrusive speech quality assessment using neural networks
Anderson R. Avila, Hannes Gamper, Chandan Reddy +3
Estimating the perceived quality of an audio signal is critical for many multimedia and audio processing systems. Providers strive to offer optimal and reliable services in order t…