1. [Publications](/publications)
2. Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
 
 # Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition

  ![](/sites/default/files/styles/wide/public/publications/Screenshot%202025-03-06%20at%201.33.18%20AM.png?itok=9Ho3pKn9)

 Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen, pretrained automatic speech recognition (ASR) model. To explore new capabilities in language modeling for speech processing, we introduce the generative speech transcription error correction (GenSEC) challenge. This challenge comprises three post-ASR language modeling tasks: (i) post-ASR transcription correction, (ii) speaker tagging, and (iii) emotion recognition. These tasks aim to emulate future LLM-based agents handling voice-based interfaces while remaining accessible to a broad audience by utilizing open pretrained language models or agent-based APIs. We also discuss insights from baseline evaluations, as well as lessons learned for designing future evaluations.



 ## Authors



[Huck Yang](/person/huck-yang)

Taejin Park (NVIDIA)

Yuan Gong (XAI)

Yuanchao Li (U Edinburgh )

Zhehuai Chen (NVIDIA)

yen-ting Lin (NVIDIA)

Chen Chen (NVIDIA)

Yuchen Hu (NVIDIA)

Kunal Dhawan (NVIDIA)

Piotr Zelasko (NVIDIA)

Chao Zhang (tsinghua university)

Yun-Nung Chen (national taiwan university)

Yu Tsao (national taiwan university)

Jagadeesh Balam (NVIDIA)

Boris Ginsburg (NVIDIA)

Shinji Watanabe (cmu)

Andreas Stolcke (ICSI)

 

 

 ## Publication Date



Tuesday, December 24, 2024

 

 ## Published in



[SLT 2024](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10832176)

 

 ## Research Area



[Natural Language Processing](/research-area/natural-language-processing)

[Speech Processing](/research-area/speech-processing)