activity
20242026
collaborators

9 papers

eess.AS2026

Doctor or Patient? Synergizing Diarization and ASR for Code-Switched Hinglish Medical Conditions Extraction

Séverin Baroudi, Yanis Labrak, Shashi Kumar +7

Extracting patient medical conditions from code-switched clinical spoken dialogues is challenging due to rapid turn-taking and highly overlapped speech. We present a robust system…

cs.CL2025

Unifying Global and Near-Context Biasing in a Single Trie Pass

Iuliia Thorbecke, Esaú Villatoro-Tello, Juan Zuluaga-Gomez +9

Despite the success of end-to-end automatic speech recognition (ASR) models, challenges persist in recognizing rare, out-of-vocabulary words - including named entities (NE) - and i…

cs.CL2024

Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction

Sergio Burdisso, Srikanth Madikeri, Petr Motlicek

Efficiently deriving structured workflows from unannotated dialogs remains an underexplored and formidable challenge in computational linguistics. Automating this process could sig…

cs.AI2024

Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

Dairazalia Sánchez-Cortés, Sergio Burdisso, Esaú Villatoro-Tello +1

Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain…

cs.CL2024

TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR

Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez +6

In traditional conversational intelligence from speech, a cascaded pipeline is used, involving tasks such as voice activity detection, diarization, transcription, and subsequent pr…

eess.AS2024

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez +5

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition on finetuning, even with limited in-domain supervised data. However, popular pretr…