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20202026
most citedExpressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer

2 citations · 2 across the 7 of their papers we have counts for

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eess.AS2026

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…

eess.AS2026

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…

eess.AS2025

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,…

eess.AS2025

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…

eess.AS2024

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…

eess.AS2024

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…