82 citations · 85 across the 8 of their papers we have counts for
7 papers · 1 filter
Optimizing Speech Language Models for Acoustic Consistency
Morteza Rohanian, Michael Krauthammer
We study speech language models that incorporate semantic initialization and planning losses to achieve robust and consistent generation. Our approach initializes speech tokens wit…
Towards Scalable and Cross-Lingual Specialist Language Models for Oncology
Morteza Rohanian, Tarun Mehra, Nicola Miglino +3
Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for d…
Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrum
Morteza Rohanian, Roya M. Hüppi, Farhad Nooralahzadeh +8
Capturing subtle speech disruptions across the psychosis spectrum is challenging because of the inherent variability in speech patterns. This variability reflects individual differ…
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Amos Calamida, Farhad Nooralahzadeh, Morteza Rohanian +3
We propose a new automated evaluation metric for machine-generated radiology reports using the successful COMET architecture adapted for the radiology domain. We train and publish…
Boosting Radiology Report Generation by Infusing Comparison Prior
Sanghwan Kim, Farhad Nooralahzadeh, Morteza Rohanian +5
Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often la…
Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs
Morteza Rohanian, Julian Hough, Matthew Purver
We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagn…