2 citations · 2 across the 7 of their papers we have counts for
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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…
TRACE-EVC: Text-Guided Relative Affective Control for Zero-Shot Emotional Voice Conversion
Zihan Zhang, Shreeram Suresh Chandra, Zongyang Du +5
Traditional emotional voice conversion (EVC) conditions generation on explicit target emotions like labels or references, defining the target affective state but omitting the direc…
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,…
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…
Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline
Ali N. Salman, Zongyang Du, Shreeram Suresh Chandra +3
Voice conversion (VC) research traditionally depends on scripted or acted speech, which lacks the natural spontaneity of real-life conversations. While natural speech data is limit…
Exploring speech style spaces with language models: Emotional TTS without emotion labels
Shreeram Suresh Chandra, Zongyang Du, Berrak Sisman
Many frameworks for emotional text-to-speech (E-TTS) rely on human-annotated emotion labels that are often inaccurate and difficult to obtain. Learning emotional prosody implicitly…