6 citations · 31 across the 23 of their papers we have counts for
24 papers
Towards Zero-Shot Attribution of Synthetic Speech via Audio-Text Contrastive Retrieval
Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu +3
Audio deepfake forensics is moving beyond a simple real-or-fake verdict toward attribution: which system generated the audio clip? Most source-attribution methods cast this as clos…
Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding
Gabriel Pirlogeanu, Dan Oneata, Horia Cucu +1
In many low-resource settings, even just eliciting speech for data collection is difficult. One promising approach has been to ask speakers to describe images. But how do we build…
Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin
Octavian Pascu, Dan Oneata, Horia Cucu +1
Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers. The latter need reliable tools to verify the authenticity of their sou…
Connecting Speech to Words through Images
Gabriel Pirlogeanu, Dan Oneata, Horia Cucu +1
How can we learn the mapping between written words and their spoken counterparts in the absence of explicit textual supervision? We present a visually grounded method for building…
Anchoring the Unknown: Open-Set Model Attribution via Proxy-Anchor Learning
Cristian-Teodor Neamtu, Serban Mihalache, Stefan Smeu +3
The proliferation of text-to-speech (TTS) systems capable of generating realistic synthetic speech poses growing challenges for audio forensics. While binary deepfake detection has…
How Open is Open TTS? A Practical Evaluation of Open Source TTS Tools
Teodora Răgman, Adrian Bogdan Stânea, Horia Cucu +1
Open-source text-to-speech (TTS) frameworks have emerged as highly adaptable platforms for developing speech synthesis systems across a wide range of languages. However, their appl…
Understanding the strengths and weaknesses of SSL models for audio deepfake model attribution
Gabriel Pîrlogeanu, Adriana Stan, Horia Cucu
Audio deepfake model attribution aims to mitigate the misuse of synthetic speech by identifying the source model responsible for generating a given audio sample, enabling accountab…
Echoes: A semantically-aligned music deepfake detection dataset
Octavian Pascu, Dan Oneata, Horia Cucu +1
We introduce Echoes, a new dataset for music deepfake detection designed for training and benchmarking detectors under realistic and provider-diverse conditions. Echoes comprises 4…
Open Source State-Of-the-Art Solution for Romanian Speech Recognition
Gabriel Pirlogeanu, Alexandru-Lucian Georgescu, Horia Cucu
In this work, we present a new state-of-the-art Romanian Automatic Speech Recognition (ASR) system based on NVIDIA's FastConformer architecture--explored here for the first time in…
Unmasking real-world audio deepfakes: A data-centric approach
David Combei, Adriana Stan, Dan Oneata +2
The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific pur…
Easy, Interpretable, Effective: openSMILE for voice deepfake detection
Octavian Pascu, Dan Oneata, Horia Cucu +1
In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset -- a de facto standard in the field of voice authenticity and deepfake detection -- can be identified wit…
WavLM model ensemble for audio deepfake detection
David Combei, Adriana Stan, Dan Oneata +1
Audio deepfake detection has become a pivotal task over the last couple of years, as many recent speech synthesis and voice cloning systems generate highly realistic speech samples…
Towards generalisable and calibrated synthetic speech detection with self-supervised representations
Octavian Pascu, Adriana Stan, Dan Oneata +2
Generalisation -- the ability of a model to perform well on unseen data -- is crucial for building reliable deepfake detectors. However, recent studies have shown that the current…
Adaptation of Whisper models to child speech recognition
Rishabh Jain, Andrei Barcovschi, Mariam Yiwere +2
Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech due to the lack of large child speech datasets required to accurately train child-friendly…
Adaptive Planning Search Algorithm for Analog Circuit Verification
Cristian Manolache, Cristina Andronache, Alexandru Caranica +4
Integrated circuit verification has gathered considerable interest in recent times. Since these circuits keep growing in complexity year by year, pre-Silicon (pre-SI) verification…
FlexLip: A Controllable Text-to-Lip System
Dan Oneata, Beata Lorincz, Adriana Stan +1
The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching cl…
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference
Catalin Visan, Octavian Pascu, Marius Stanescu +5
With the ever increasing complexity of specifications, manual sizing for analog circuits recently became very challenging. Especially for innovative, large-scale circuits designs,…
Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations
Dan Oneata, Horia Cucu
Multimodal speech recognition aims to improve the performance of automatic speech recognition (ASR) systems by leveraging additional visual information that is usually associated t…
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis
Rishabh Jain, Mariam Yiwere, Dan Bigioi +2
Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech. However, most of the TTS research focuses on using adult…
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition
Rishabh Jain, Andrei Barcovschi, Mariam Yiwere +3
Despite recent advancements in deep learning technologies, Child Speech Recognition remains a challenging task. Current Automatic Speech Recognition (ASR) models require substantia…
Speaker disentanglement in video-to-speech conversion
Dan Oneata, Adriana Stan, Horia Cucu
The task of video-to-speech aims to translate silent video of lip movement to its corresponding audio signal. Previous approaches to this task are generally limited to the case of…
An evaluation of word-level confidence estimation for end-to-end automatic speech recognition
Dan Oneata, Alexandru Caranica, Adriana Stan +1
Quantifying the confidence (or conversely the uncertainty) of a prediction is a highly desirable trait of an automatic system, as it improves the robustness and usefulness in downs…
The Quo Vadis submission at Traffic4cast 2019
Dan Oneata, Cosmin George Alexandru, Marius Stanescu +4
We describe the submission of the Quo Vadis team to the Traffic4cast competition, which was organized as part of the NeurIPS 2019 series of challenges. Our system consists of a tem…
Kite: Automatic speech recognition for unmanned aerial vehicles
Dan Oneata, Horia Cucu
This paper addresses the problem of building a speech recognition system attuned to the control of unmanned aerial vehicles (UAVs). Even though UAVs are becoming widespread, the ta…