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20242026
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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

Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions

Abinay Reddy Naini, Jaeyeon Kim, Chao-Han Huck Yang +2

Large audio-language models (LALMs) can reason about audio, yet it remains unclear whether they can perform comparative judgments between two speech signals along emotional, enviro…

eess.AS2026

Recovering Performance in Speech Emotion Recognition from Discrete Tokens via Multi-Layer Fusion and Paralinguistic Feature Integration

Esther Sun, Abinay Reddy Naini, Carlos Busso

Discrete speech tokens offer significant advantages for storage and language model integration, but their application in speech emotion recognition (SER) is limited by paralinguist…

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

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 the rich prosodic variations inherent in human speech is essential for generating natural-sounding speech. While speaker embeddings are commonly used as conditioning input…

eess.AS2025

The MSP-Podcast Corpus

Carlos Busso, Reza Lotfian, Kusha Sridhar +9

The availability of large, high-quality emotional speech databases is essential for advancing speech emotion recognition (SER) in real-world scenarios. However, many existing datab…