2 citations · 3 across the 10 of their papers we have counts for
6 papers · 1 filter
Towards Language-Independent Face-Voice Association with Multimodal Foundation Models
Aref Farhadipour, Teodora Vukovic, Volker Dellwo
This paper describes the UZH-CL system submitted to the FAME2026 Challenge. The challenge focuses on cross-modal verification under unique multilingual conditions, specifically uns…
Investigating Identity Signals in Conversational Facial Dynamics via Disentangled Expression Features
Masoumeh Chapariniya, Pierre Vuillecard, Jean-Marc Odobez +2
This work investigates whether individuals can be identified solely through the pure dynamical components of their facial expressions, independent of static facial appearance. We l…
CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation
Aref Farhadipour, Shiran Liu, Masoumeh Chapariniya +4
The CL-UZH team submitted one system each for the fixed and open conditions of the NIST SRE 2024 challenge. For the closed-set condition, results for the audio-only trials were ach…
Beyond Appearance: Transformer-based Person Identification from Conversational Dynamics
Masoumeh Chapariniya, Teodora Vukovic, Sarah Ebling +1
This paper investigates the performance of transformer-based architectures for person identification in natural, face-to-face conversation scenario. We implement and evaluate a two…
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts
Aref Farhadipour, Hossein Ranjbar, Masoumeh Chapariniya +3
Emotion recognition and sentiment analysis are pivotal tasks in speech and language processing, particularly in real-world scenarios involving multi-party, conversational data. Thi…
Two-Stream Spatial-Temporal Transformer Framework for Person Identification via Natural Conversational Keypoints
Masoumeh Chapariniya, Hossein Ranjbar, Teodora Vukovic +2
In the age of AI-driven generative technologies, traditional biometric recognition systems face unprecedented challenges, particularly from sophisticated deepfake and face reenactm…