16 citations · 16 across the 14 of their papers we have counts for
14 papers
Not All Attacks Are Learned Equally in Speech Deepfake Detection
Avantika Singh, Aurosweta Mahapatra, Ismail Rasim Ulgen +3
Speech deepfake detection (SDD) models are trained on multi-attack datasets containing diverse spoofing systems, such as text-to-speech (TTS) and voice conversion (VC). In standard…
Is Prosody Lost in Translation? Fine-Grained Cross-Lingual Prosody Similarity Across Languages
Haopeng Xie, Ismail Rasim Ulgen, Sofia Son +2
Prosody plays an important role in speech translation, conveying information such as emphasis, emotion, and intent beyond lexical content. However, despite recent progress in expre…
AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks
Aurosweta Mahapatra, Xiutian Zhao, Shreeram Suresh Chandra +7
Speech deepfake detection (SDD) systems achieve strong performance on conventional benchmarks; however, existing datasets provide limited coverage of emotionally expressive and rec…
ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks
Aurosweta Mahapatra, Ismail Rasim Ulgen, Kong Aik Lee +2
Speech deepfake detection (SDD) systems perform well on standard benchmarks datasets but often fail to generalize to expressive and emotional spoofing attacks. Many methods rely on…
DiffAnon: Diffusion-based Prosody Control for Voice Anonymization
Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews +2
To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing meth…
Neuron-Level Emotion Control in Speech-Generative Large Audio-Language Models
Xiutian Zhao, Ismail Rasim Ulgen, Philipp Koehn +2
Large audio-language models (LALMs) can produce expressive speech, yet reliable emotion control remains elusive: conversions often miss the target affect and may degrade linguistic…
NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion
Zongyang Du, Shreeram Suresh Chandra, Ismail Rasim Ulgen +4
Everyday speech conveys far more than words, it reflects who we are, how we feel, and the circumstances surrounding our interactions. Yet, most existing speech datasets are acted,…
HuLA: Prosody-Aware Anti-Spoofing with Multi-Task Learning for Expressive and Emotional Synthetic Speech
Aurosweta Mahapatra, Ismail Rasim Ulgen, Berrak Sisman
Current anti-spoofing systems remain vulnerable to expressive and emotional synthetic speech, since they rarely leverage prosody as a discriminative cue. Prosody is central to huma…
Objective Evaluation of Prosody and Intelligibility in Speech Synthesis via Conditional Prediction of Discrete Tokens
Ismail Rasim Ulgen, Zongyang Du, Junchen Lu +2
Objective evaluation of synthesized speech is critical for advancing speech generation systems, yet existing metrics for intelligibility and prosody remain limited in scope and wea…
Can Emotion Fool Anti-spoofing?
Aurosweta Mahapatra, Ismail Rasim Ulgen, Abinay Reddy Naini +2
Traditional anti-spoofing focuses on models and datasets built on synthetic speech with mostly neutral state, neglecting diverse emotional variations. As a result, their robustness…
Discrete Unit based Masking for Improving Disentanglement in Voice Conversion
Philip H. Lee, Ismail Rasim Ulgen, Berrak Sisman
Voice conversion (VC) aims to modify the speaker's identity while preserving the linguistic content. Commonly, VC methods use an encoder-decoder architecture, where disentangling t…
Text-to-Speech for Unseen Speakers via Low-Complexity Discrete Unit-Based Frame Selection
Ismail Rasim Ulgen, Shreeram Suresh Chandra, Junchen Lu +1
Synthesizing the voices of unseen speakers remains a persisting challenge in multi-speaker text-to-speech (TTS). Existing methods model speaker characteristics through speaker cond…
Rethinking Speaker Embeddings for Speech Generation: Sub-Center Modeling for Capturing Intra-Speaker Diversity
Ismail Rasim Ulgen, John H. L. Hansen, Carlos Busso +1
Modeling speech variation is key to natural, expressive generation. Speaker embeddings are commonly used to condition personalized speech systems, but they are typically trained fo…
Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognition
Ismail Rasim Ulgen, Zongyang Du, Carlos Busso +1
Speaker embeddings carry valuable emotion-related information, which makes them a promising resource for enhancing speech emotion recognition (SER), especially with limited labeled…