Summary of Speaker Tagging Correction with Non-autoregressive Language Models, by Grigor Kirakosyan et al.
Speaker Tagging Correction With Non-Autoregressive Language Models
by Grigor Kirakosyan, Davit Karamyan
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents an innovative approach for solving the task of assigning spoken words to their respective speakers in conversations. By leveraging automatic speech recognition (ASR) and speaker diarization (SD) systems, researchers aim to develop a unified framework that accurately identifies who spoke when. The existing speaker diarization systems often face performance degradation due to various factors such as uniform segmentation, inaccurate timestamps, incorrect clustering, and background noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper focuses on developing an improved speaker diarization system that can better handle real-world challenges. It presents a unified framework for assigning spoken words to speakers in conversations, which can be used in practical settings such as speech applications dealing with conversations. |
Keywords
» Artificial intelligence » Clustering
