16 citations · 30 across the 17 of their papers we have counts for
17 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…
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…
ADEPT: RL-Aligned Agentic Decoding of Emotion via Evidence Probing Tools -- From Consensus Learning to Ambiguity-Driven Emotion Reasoning
Esther Sun, Bo-Hao Su, Abinay Reddy Naini +2
Speech Large Language Models (SLLMs) enable high-level emotion reasoning but often produce ungrounded, text-biased judgments without verifiable acoustic evidence. In contrast, self…
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…
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,…
Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation
Karen Rosero, Eunjung Yeo, David R. Mortensen +3
We present ChiReSSD, a speech reconstruction framework that preserves children speaker's identity while suppressing mispronunciations. Unlike prior approaches trained on healthy ad…
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…
RankList -- A Listwise Preference Learning Framework for Predicting Subjective Preferences
Abinay Reddy Naini, Fernando Diaz, Carlos Busso
Preference learning has gained significant attention in tasks involving subjective human judgments, such as \emph{speech emotion recognition} (SER) and image aesthetic assessment.…
FedMLAC: Mutual Learning Driven Heterogeneous Federated Audio Classification
Jun Bai, Rajib Rana, Di Wu +5
Federated Learning (FL) offers a privacy-preserving framework for training audio classification (AC) models across decentralized clients without sharing raw data. However, Federate…
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…
EmotionRankCLAP: Bridging Natural Language Speaking Styles and Ordinal Speech Emotion via Rank-N-Contrast
Shreeram Suresh Chandra, Lucas Goncalves, Junchen Lu +2
Current emotion-based contrastive language-audio pretraining (CLAP) methods typically learn by naïvely aligning audio samples with corresponding text prompts. Consequently, this ap…
Towards Emotionally Consistent Text-Based Speech Editing: Introducing EmoCorrector and The ECD-TSE Dataset
Rui Liu, Pu Gao, Jiatian Xi +3
Text-based speech editing (TSE) modifies speech using only text, eliminating re-recording. However, existing TSE methods, mainly focus on the content accuracy and acoustic consiste…
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition
Shreya G. Upadhyay, Ali N. Salman, Carlos Busso +1
Cross-corpus speech emotion recognition (SER) plays a vital role in numerous practical applications. Traditional approaches to cross-corpus emotion transfer often concentrate on ad…
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…
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…
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
Thejan Rajapakshe, Rajib Rana, Sara Khalifa +3
Speech Emotion Recognition (SER) is crucial for enabling computers to understand the emotions conveyed in human communication. With recent advancements in Deep Learning (DL), the p…
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…