16 citations · 18 across the 11 of their papers we have counts for
11 papers
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…
Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model
Zongyang Du, Junchen Lu, Kun Zhou +2
Expressive voice conversion (VC) conducts speaker identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Emotional style modeling 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…
Disentanglement of Emotional Style and Speaker Identity for Expressive Voice Conversion
Zongyang Du, Berrak Sisman, Kun Zhou +1
Expressive voice conversion performs identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Due to the hierarchical structure of spe…
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer
Zongyang Du, Berrak Sisman, Kun Zhou +1
Traditional voice conversion(VC) has been focused on speaker identity conversion for speech with a neutral expression. We note that emotional expression plays an essential role in…
Spectrum and Prosody Conversion for Cross-lingual Voice Conversion with CycleGAN
Zongyang Du, Kun Zhou, Berrak Sisman +1
Cross-lingual voice conversion aims to change source speaker's voice to sound like that of target speaker, when source and target speakers speak different languages. It relies on n…