Papers by Karthik S
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR (2024.emnlp-main)
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Shashi Kumar, Srikanth Madikeri, Juan Pablo Zuluaga Gomez, Iuliia Thorbecke, Esaú Villatoro-tello, Sergio Burdisso, Petr Motlicek, Karthik S, Aravind Ganapathiraju
| Challenge: | Existing approaches to automatic speech recognition use cascaded pipelines for tasks like voice activity detection, diarization, transcription and subsequent processing. |
| Approach: | They propose a single Transducer-based model that integrates task-specific tokens into the reference text during ASR model training, streamlining inference and eliminating the need for separate NLP models. |
| Outcome: | The proposed model outperforms the existing pipeline on speaker change detection, endpointing, and NER tasks while outperforming the existing model in individual task performance. |
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper (2024.findings-emnlp)
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Iuliia Thorbecke, Juan Pablo Zuluaga Gomez, Esaú Villatoro-tello, Shashi Kumar, Pradeep Rangappa, Sergio Burdisso, Petr Motlicek, Karthik S, Aravind Ganapathiraju
| Challenge: | a recent study shows that training of ASR models with little to no supervised data is challenging. |
| Approach: | They propose a framework to train streaming Transformer-Transducer models with pseudo-labeled (PL) speech from foundational speech models. |
| Outcome: | The proposed framework can be trained from scratch with pseudo-labeled speech from foundational speech models (FSMs) the proposed framework is validated on 6 languages from CommonVoice and proposes multiple heuristics to filter out hallucinated PLs. |