11 papers · 1 filter
Word-Aligned Prosody Reflects Speaker Differences More Than Depression or PTSD Severity
Hsiang-Chen Yeh, Aurosweta Mahapatra, Shreeram Suresh Chandra +2
Word-aligned prosody is increasingly used to detect depression and posttraumatic stress disorder (PTSD), yet the stability and clinical relevance of these measurements remain uncle…
Brain2Speech-Net: Fast and Intelligible Brain-to-Speech Synthesis Without Text Decoding
Shreeram Suresh Chandra, Zexin Cai, Yu Tsao +2
The loss of speech limits communication for individuals with paralysis. Direct neural-to-speech synthesis is challenging due to the limited availability of neural data for training…
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…
Disentangling the Interpretive and Predictive Roles of LIWC: Controlled Substitution in Depression-Related Classification
Hsiang-Chen Yeh, Xiutian Zhao, Aurosweta Mahapatra +3
Linguistic Inquiry and Word Count (LIWC) provides auditable psycholinguistic categories that are widely used to interpret depression-related language, but its incremental predictiv…
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…
Who is Speaking or Who is Depressed? A Controlled Study of Speaker Leakage in Speech-Based Depression Detection
Hsiang-Chen Yeh, Luqi Sun, Aurosweta Mahapatra +3
This study investigates whether speech-based depression detection models learn depression-related acoustic biomarkers or instead rely on speaker identity cues. Using the DAIC-WOZ d…
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,…
Text-to-Speech for Unseen Speakers via Low-Complexity Discrete Unit-Based Frame Selection
Ismail Rasim Ulgen, Shreeram Suresh Chandra, Junchen Lu +1
Synthesizing the voices of unseen speakers remains a persisting challenge in multi-speaker text-to-speech (TTS). Existing methods model speaker characteristics through speaker cond…
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…
Style Mixture of Experts for Expressive Text-To-Speech Synthesis
Ahad Jawaid, Shreeram Suresh Chandra, Junchen Lu +1
Recent advances in style transfer text-to-speech (TTS) have improved the expressiveness of synthesized speech. However, encoding stylistic information (e.g., timbre, emotion, and p…
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…